Matthew J. Daigle
Lead, Diagnostics & Prognostics Group / Research Computer Scientist, Intelligent Systems Division, NASA Ames Research Center
Forthcoming
  • M. Daigle, I. Roychoudhury, L. Spirkovska, K. Goebel, S. Sankararaman, J. Ossenfort, and C. Kulkarni, Real-Time Prediction of Safety Margins in the National Airspace," AIAA Aviation Conference, Denver, CO, to appear. [show abstract]
    Abstract: Underlying all operations in the National Airspace System (NAS) is the concept of safety. Safety, as defined by acceptable levels of risk, is to be maintained at all times. The real-time safety monitoring (RTSM) framework is under development to provide an automated system to quantify safety in the NAS, estimate the current level of safety, and predict the future evolution of safety and the occurrence of events that pose an increased risk to flights so that these occurrences can be managed strategically rather than mitigated reactively. This paper presents the mathematical framework, the models, and the monitoring and prediction algorithms used to achieve this. RTSM computes safety as expressed through a set of safety margins based on user-defined safety metrics, thresolds, and events. Sources of uncertainty are modeled and propagated through the predictions in order to compute the probabilistic evolution of safety and the probability of events that introduce increased risk to operations. A prototype implementation is discussed and results demonstrating feasibility are presented. The results highlight the kinds of predictions that can be computed and the fidelity that is currently achieved.
  • L. Spirkovska, I. Roychoudhury, M. Daigle, and K. Goebel, Real Time Safety Monitoring: Concept for Supporting Safe Flight Operations," AIAA Aviation Conference, Denver, CO, to appear. [show abstract]
    Abstract: Processes, procedures, regulations, and technologies are continuously evolving to maintain or improve the safety of the National Airspace System (NAS). In this paper, we describe a Real Time Safety Monitoring (RTSM) system that benefits from these efforts to define a set of safety metrics that are automatically monitored in real-time. In addition to providing information about current potentially adverse conditions to a variety of users, from those who need a broad overview of a day's flight operations to those who need to decide on a control tactic to employ in the next five minutes, the RTSM system predicts conditions within a specified prediction horizon. Its intelligent interface alerts the user, presenting the information as appropriate considering the current context and circumstances. We illustrate the system concept with five conceptual use cases, describing which safety metrics may be of the most interest to five user groups and suggesting a multi-modal display format. We posit that having access to information about adverse conditions in time to make efficient preemptive decisions without sacrificing safety will improve the already high level of safety and aid in the expansion planned for the NAS under the Next Generation Air Transportation System (NextGen).
2017
  • K. Goebel, M. Daigle, A. Saxena, S. Sankararaman, I. Roychoudhury, and J. Celaya, "Prognostics: The Science of Making Predictions," April 2017. [show abstract]
    Abstract: Prognostics is the science of making predictions of engineering systems. It is part of a suite of techniques that determine whether a system is behaving within nominal operational bounds and – if it does not – that determine what is wrong and how long it will take until the system no longer fulfills certain functional requirements. This book presents the latest developments and research findings on the topic of prognostics by the Prognostics Center of Excellence at NASA Ames Research Center. The book is intended to provide a practitioner with an understanding of the foundational concepts as well as practical tools to perform prognostics and health management on different types of engineering systems and in particular to predict remaining useful life.
  • S. Sankararaman and M. Daigle, "Uncertainty Quantification in Trajectory Prediction for Aircraft Operations," AIAA Guidance, Navigation, and Control Conference, Grapevine, TX, January 2017. [show abstract]
    Abstract: This paper presents a computational methodology for uncertainty quantification in predicting the trajectory of a generic, realistic aircraft based on information regarding flight plan, aircraft information, wind and weather information, etc. Predicting the trajectory of aircraft is important from the point of view of analyzing and predicting the safety of the overall airspace, and making risk-informed decisions regarding the operations of the airspace. The proposed methodology is based on using first-principles for analyzing the motion of the aircraft and estimating its future trajectory. Since the core of this problem lies in predicting the future behavior of a generic aircraft, it is essential to understand that it is almost impossible to precisely predict the future trajectory with certainty. Hence, an intuitive approach is to analyze the various sources of uncertainty that affect the aircraft prediction and quantify their combined effect on the whole trajectory. Further, this paper develops a global sensitivity analysis-based methodology to quantify the relative contributions of the various sources of uncertainty to the uncertainty of the overall trajectory. The proposed methodology is illustrated using a numerical example consisting of an aircraft that takes off from the San Francisco International Airport.
2016
  • M. Daigle and S. Sankararaman, "Predicting Remaining Driving Time and Distance of a Planetary Rover under Uncertainty," ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, vol. 2, no. 4, December 2016. [show abstract]
    Abstract: The operations of a planetary rover depend critically upon the amount of power that can be delivered by its batteries. In order to plan the future operation, it is important to make reliable predictions regarding the end-of-discharge time, which can be used to estimate the remaining driving time and distance. These quantities are stochastic in nature, not only because there are several sources of uncertainty that affect the rover's operation, but also since the future operating conditions cannot be known precisely. This paper presents a computational methodology to predict these stochastic quantities, based on a model of the rover and its batteries. We utilize a model-based prognostics framework that characterizes and incorporates the various sources of uncertainty into these predictions, thereby assisting operational decision-making. We consider two different types of driving scenarios, and develop methods for each to characterize the associated uncertainty. Monte Carlo sampling and the inverse first-order reliability method are used to compute the stochastic predictions of end-of-discharge time, remaining driving time, and remaining driving distance.
  • M. Daigle, A. Bregon, and I. Roychoudhury, "A Qualitative Fault Isolation Approach for Parametric and Discrete Faults Using Structural Model Decomposition," Annual Conference of the Prognostics and Health Management Society 2016, pp. 413-425, Denver, CO, October 2016. [show abstract]
    Abstract: With increasing complexity of engineering systems, fault diagnostics plays a significant role in ensuring that they operate safely. Such systems most often exhibit mixed discrete and continuous, i.e., hybrid, behavior, and may encounter both parametric faults (unexpected changes in system parameters) as well as discrete faults (unexpected changes in component modes). Diagnosis becomes computationally very complex due to the large number of possible system modes, and possible mode changes that occur near the point of fault occurrence. This paper presents a qualitative fault isolation framework for integrated diagnosis of both parametric and discrete faults in hybrid systems, based on structural model decomposition. Fault isolation is performed by analyzing the qualitative information of the residual deviations, and considering observation delay. The great advantage of structural model decomposition for this problem is that it essentially defines several smaller independent diagnosis problems that become more efficient to solve, and makes the overall diagnosis problem more scalable. To demonstrate and test the validity of our approach, we use a hydraulic multi-tank system as the case study in simulation. Results illustrate that the approach is both efficient and scalable.
  • M. Daigle, S. Sankararaman, and I. Roychoudhury, "System-level Prognostics for the National Airspace," Annual Conference of the Prognostics and Health Management Society 2016, pp. 397-405, Denver, CO, October 2016. [show abstract]
    Abstract: In the National Airspace System (NAS), safety is assured through a set of rules, regulations, and procedures to respond to unsafe events. However, safety stands to benefit immensely from the introduction of tools and methodologies from Prognostics and Health Management (PHM). PHM will enable the NAS to stochastically predict unsafe states within the NAS, enabling a proactive preventative response strategy, as opposed to a reactive mitigative one. However, current PHM methods do not directly apply to the NAS for several reasons: they typically apply only at the component level, are implemented in a centralized manner, and are focused only on predicting remaining useful life. In this paper, we extend the model-based prognostics approach to PHM in order to provide a framework that can be applied to the NAS. We offer a system-level approach that supports a distributed implementation, and provide algorithms to predict the probability of an unsafe state, either at a specific time or within a time interval, and to predict the time of an unsafe state. Experimental results in simulation demonstrate the new approach.
  • M. Daigle, A. Bregon, X. Koutsoukos, G. Biswas, and B. Pulido, "A Qualitative Event-based Approach to Multiple Fault Diagnosis in Continuous Systems using Structural Model Decomposition," Engineering Applications of Artificial Intelligence, vol. 53, pp. 190-206, August 2016. [show abstract]
    Abstract: Multiple fault diagnosis is a difficult problem for dynamic systems, and, as a result, most multiple fault diagnosis approaches are restricted to static systems, and most dynamic system diagnosis approaches make the single fault assumption. Within the framework of consistency-based diagnosis, the challenge is to generate conflicts from dynamic signals. For multiple faults, this becomes difficult due to the possibility of fault masking and different relative times of fault occurrence, resulting in many different ways that any given combination of faults can manifest in the observations. In order to address these challenges, we develop a novel multiple fault diagnosis framework for continuous dynamic systems. We construct a qualitative event-based framework, in which discrete qualitative symbols are generated from residual signals. Within this framework, we formulate an online diagnosis approach and establish definitions of multiple fault diagnosability. Residual generators are constructed based on structural model decomposition, which, as we demonstrate, has the effect of reducing the impact of fault masking by decoupling faults from residuals, thus improving diagnosability and fault isolation performance. Through simulation-based multiple fault diagnosis experiments, we demonstrate and validate the concepts developed here, using a multi-tank system as a case study.
  • A. Bregon, M. Daigle, and I. Roychoudhury, "Qualitative Fault Isolation of Hybrid Systems: A Structural Model Decomposition-Based Approach," Third European Conference of the PHM Society 2016, pp. 515-525, Bilbao, Spain, July 2016. [show abstract]
    Abstract: Quick and robust fault diagnosis is critical to ensuring safe operation of complex engineering systems. A large number of techniques are available to provide fault diagnosis in systems with continuous dynamics. However, many systems in aerospace and industrial environments are best represented as hybrid systems that consist of discrete behavioral modes, each with its own continuous dynamics. These hybrid dynamics make the on-line fault diagnosis task computationally more complex due to the large number of possible system modes and the existence of autonomous mode transitions. This paper presents a qualitative fault isolation framework for hybrid systems based on structural model decomposition. The fault isolation is performed by analyzing the qualitative information of the residual deviations. However, in hybrid systems this process becomes complex due to possible existence of observation delays, which can cause observed deviations to be inconsistent with the expected deviations for the current mode in the system. The great advantage of structural model decomposition is that (i) it allows to design residuals that respond to only a subset of the faults, and (ii) every time a mode change occurs, only a subset of the residuals will need to be reconfigured, thus reducing the complexity of the reasoning process for isolation purposes. To demonstrate and test the validity of our approach, we use an electric circuit simulation as the case study.
  • I. Roychoudhury, M. Daigle, K. Goebel, L. Spirkovska, S. Sankararaman, J. Ossenfort, C. Kulkarni, W. McDermott, and S. Poll, Initial Demonstration of the Real-time Safety Monitoring Framework for the National Airspace System Using Flight Data," 16th AIAA Aviation Technology, Integration, and Operations Conference, Washington, D.C., June 2016. [show abstract]
    Abstract: As new operational paradigms and additional aircraft are being introduced into the National Airspace System (NAS), maintaining safety in such a rapidly growing environment becomes more challenging. It is therefore desirable to have an automated framework to provide an overview of the current safety of the airspace at different levels of granularity, as well an understanding of how the state of the safety will evolve into the future given the anticipated flight plans, weather forecast, predicted health of assets in the airspace, and so on. Towards this end, as part of our earlier work, we formulated the Real-Time Safety Monitoring (RTSM) framework for monitoring and predicting the state of safety and to predict unsafe events. In our previous work, the RTSM framework was demonstrated in simulation on three different constructed scenarios. In this paper, we further develop the framework and demonstrate it on real flight data from multiple data sources. Specifically, the flight data is obtained through the Shadow Mode Assessment using Realistic Technologies for the National Airspace System (SMART-NAS) Testbed that serves as a central point of collection, integration, and access of information from these different data sources. By testing and evaluating using real-world scenarios, we may accelerate the acceptance of the RTSM framework towards deployment. In this paper, using the RTSM framework, we demonstrate its capability to not only estimate the state of safety in the NAS, but predict the time and location of unsafe events such as a loss of separation between two aircraft, or an aircraft encountering convective weather. The experimental results highlight the accuracy of the approach, and the kind of information that can be provided to operators to improve their situational awareness in the context of safety.
  • Q. Gaudel, P. Ribot, E. Chanthery, and M. Daigle, "Health Monitoring of a Planetary Rover Using Hybrid Particle Petri Nets," 37th International Conference on Applications and Theory of Petri Nets and Concurrency, pp. 196-215, Torun, Poland, June 2016. [show abstract]
    Abstract: This paper focuses on the application of a Petri Net-based diagnosis method on a planetary rover prototype. The diagnosis is performed by using a model-based method in the context of health management of hybrid systems. In system health management, the diagnosis task aims at determining the current health state of a system and the fault occurrences that lead to this state. The Hybrid Particle Petri Nets (HPPN) formalism is used to model hybrid systems behavior and degradation, and to define the generation of diagnosers to monitor the health states of such systems under uncertainty. At any time, the HPPN-based diagnoser provides the current diagnosis represented by a distribution of beliefs over the health states. The health monitoring methodology is demonstrated on the K11 rover. A hybrid model of the K11 is proposed and experimental results show that the approach is robust to real system data and constraints.
  • M. Daigle and C. Kulkarni, "End-of-discharge and End-of-life Prediction in Lithium-ion Batteries with Electrochemistry-based Aging Models," AIAA Infotech@Aerospace Conference, San Diego, CA, January 2016. [show abstract]
    Abstract: As batteries become increasingly prevalent in complex systems such as aircraft and electric cars, monitoring and predicting battery state of charge and state of health becomes critical. In order to accurately predict the remaining battery power to support system operations for informed operational decision-making, age-dependent changes in dynamics must be accounted for. Using an electrochemistry-based model, we investigate how key parameters of the battery change as aging occurs, and develop models to describe aging through these key parameters. Using these models, we demonstrate how we can (\emph{i}) accurately predict end-of-discharge for aged batteries, and (\emph{ii}) predict the end-of-life of a battery as a function of anticipated usage. The approach is validated through an experimental set of randomized discharge profiles.
  • I Roychoudhury, L Spirkovska, M Daigle, E. Balaban, S. Sankararaman, C. Kulkarni, S. Poll, and K. Goebel, "Predicting Real-Time Safety of the National Airspace System," AIAA Infotech@Aerospace Conference, San Diego, CA, January 2016. [show abstract]
    Abstract: Situation awareness is necessary for operators to make informed decisions regarding avoidance of airspace hazards. To this end, each operator must consolidate operations-relevant information from disparate sources and apply extensive domain knowledge to correctly interpret the current state of the NAS as well as forecast its (combined) evolution over the duration of the NAS operation. This time- and workload-intensive process is periodically repeated throughout the operation so that changes can be managed in a timely manner. The imprecision, inaccuracy, inconsistency, and incompleteness of the incoming data further challenges the process. To facilitate informed decision making, this paper presents a model-based framework for the \textit{automated} real-time monitoring and prediction of possible effects of airspace hazards on the safety of the National Airspace System (NAS). First, hazards to flight are identified and transformed into \sm{}s, that is, quantities of interest that could be evaluated based on available data and are predictive of an unsafe event. The \sm{}s and associated thresholds that specify when an event transitions from \emph{safe} to \emph{unsafe} are combined with models of airspace operations and aircraft dynamics. The framework can include any hazard to flight that can be modeled quantitatively. Models can be detailed and complex, or they can be considerably simplifed, as appropriate to the application. Real-time NAS safety monitoring and prediction begins with an estimate of the state of the NAS using the dynamic models. Given the state estimate and a probability distribution of future inputs to the NAS, we can then predict the evolution of the NAS - the future state - and the occurrence of hazards and unsafe events. The entire probability distribution of airspace \sm{}s is computed, not just point estimates, without significant assumptions regarding the distribution type and/or parameters. We demonstrate our overall approach through a simulated scenario in which we predict the occurrence of some unsafe events and show how these predictions evolve in time as flight operations progress. Predictions accounting for common sources of uncertainty are included and it is shown how the predictions improve in time, become more confident, and change dynamically as new information is made available to the prediction algorithm.
2015
  • I. Roychoudhury, L. Spirkovska, M. Daigle, E. Balaban, S. Sankararaman, C. Kulkarni, S. Poll, and K. Goebel, "Real-Time Monitoring and Prediction of Airspace Safety," NASA/TM-2015-218928, NASA Ames Research Center, December 2015. [show abstract]
    Abstract: The U.S. National Airspace System (NAS) has reached an extremely high level of safety in recent years. However, it will only become more difficult to maintain the current level of safety with the forecasted increase in operations, and so the FAA has been making revolutionary changes to the NAS to both expand capacity and ensure safety. Our work complements these efforts by developing a novel model-based framework for real-time monitoring and prediction of the safety of the NAS. Our framework is divided into two parts: (offline) safety analysis and modeling part, and a real-time (online) monitoring and prediction of safety. The goal of the safety analysis task is to identify hazards to flight (distilled from several national databases) and to codify these hazards within our framework such that we can monitor and predict them. From these we define safety metrics that can be monitored and predicted using dynamic models of airspace operations, aircraft, and weather, along with a rigorous, mathematical treatment of uncertainty. We demonstrate our overall approach and highlight the advantages of this approach over the current state-of-the-art through simulated scenarios.
  • M. Daigle, "Real-time Prognostics of a Rotary Valve Actuator," Annual Conference of the Prognostics and Health Management Society 2015, pp. 46-56, San Diego, CA, October 2015. [show abstract]
    Abstract: Valves are used in many domains and often have system-critical functions. As such, it is important to monitor the health of valves and their actuators and predict remaining useful life. In this work, we develop a model-based prognostics approach for a rotary valve actuator. Due to limited observability of the component with multiple failure modes, a lumped damage approach is proposed for estimation and prediction of damage progression. In order to support the goal of real-time prognostics, an approach to prediction is developed that does not require online simulation to compute remaining life, rather, a function mapping the damage state to remaining useful life is found offline so that predictions can be made quickly online with a single function evaluation. Simulation results demonstrate the overall methodology, validating the lumped damage approach and demonstrating real-time prognostics.
  • M. Daigle, I. Roychoudhury, and A. Bregon, "Model-based Prognostics of Hybrid Systems," Annual Conference of the Prognostics and Health Management Society 2015, pp. 57-66, San Diego, CA, October 2015. [show abstract]
    Abstract: Model-based prognostics has become a popular approach to solving the prognostics problem. However, almost all work has focused on prognostics of systems with continuous dynamics. In this paper, we extend the model-based prognostics framework to hybrid systems models that combine both continuous and discrete dynamics. In general, most systems are hybrid in nature, including those that combine physical processes with software. We generalize the model-based prognostics formulation to hybrid systems, and describe the challenges involved. We present a general approach for modeling hybrid systems, and overview methods for solving estimation and prediction in hybrid systems. As a case study, we consider the problem of conflict (i.e., loss of separation) prediction in the National Airspace System, in which the aircraft models are hybrid dynamical systems.
  • C. Kulkarni, G. Gorospe, M. Daigle, and K. Goebel, A Testbed for Implementing Prognostic Methodologies on Cryogenic Propellant Loading Systems," IEEE Instrumentation & Measurement Magazine, vol. 18, no. 4, pp. 5-15, August 2015. [show abstract]
    Abstract: Prognostic technology determines the health state of a system and estimates its remaining useful life. With this information, operators can make decisions on how to operate the system such that it minimizes life cycle cost and maximizes safety. The individual decisions can range from changes to control settings to scheduling future operations or to determining the best repair and maintenance activities. Fault injection experiments on test beds that represent key subsystems are critical for the maturation of both the sub-systems themselves as well as the prognostic technology. It is necessary to precisely emulate actual fault conditions to maximize the utility for validation of hardware and software. Representative of such a test environment, we present here the development of a testbed for pneumatic valves, which are used in cryogenic propellant loading systems. The pneumatic valve testbed allows for the injection of magnitude-varying leaks that follow specified damage progression profiles in order to emulate the evolution (on a compressed time scale) of common valve faults. Besides the various leakage faults, the testbed also allows exploration of battery degradation on the operation of the valves. Experimental results and progress towards the maturation and validation of component-level prognostic methods are discussed in the context of cryogenic refueling operations.
  • M. Daigle, A. Bregon, and I. Roychoudhury, "A Structural Model Decomposition Framework for Hybrid Systems Diagnosis," 26th International Workshop on Principles of Diagnosis, pp. 201-208, Paris, France, August 2015. [show abstract]
    Abstract: Nowadays, a large number of practical systems in aerospace and industrial environments are best represented as hybrid systems that consist of discrete modes of behavior, each defined by a set of continuous dynamics. These hybrid dynamics make the on-line fault diagnosis task very challenging. In this work, we present a new modeling and diagnosis framework for hybrid systems. Models are composed from sets of user-defined components using a compositional modeling approach. Submodels for residual generation are then generated for a given mode, and reconfigured efficiently when the mode changes. Efficient reconfiguration is established by exploiting causality information within the hybrid system models. The submodels can then be used for fault diagnosis based on residual generation and analysis. We demonstrate the efficient causality reassignment, submodel reconfiguration, and residual generation for fault diagnosis using an electrical circuit case study.
  • M. Daigle, I. Roychoudhury, and A. Bregon, "Qualitative Event-based Diagnosis Applied to a Spacecraft Electrical Power Distribution System," Control Engineering Practice, vol. 38, pp. 75-91, May 2015. [show abstract]
    Abstract: Quick, robust fault diagnosis is critical to ensuring safe operation of complex engineering systems. A fault detection, isolation, and identification framework is developed for three separate diagnosis algorithms: the first using global model; the second using minimal submodels, which allows the approach to scale easily; and the third using both the global model and minimal submodels, combining the strengths of the first two. The diagnosis framework is applied to the Advanced Diagnostics and Prognostics Testbed, that functionally represents spacecraft electrical power distribution systems. The practical implementation of these algorithms is described, and their diagnosis performance using real data is compared.
  • A. Bregon, M. Daigle, and I. Roychoudhury, "An Integrated Framework for Distributed Diagnosis of Process and Sensor Faults," 2015 IEEE Aerospace Conference, Big Sky, MT, March 2015. [show abstract]
    Abstract: Complex engineering systems require efficient on-line fault diagnosis methodologies to improve safety and reduce maintenance costs. In complex systems, faults may occur in the process itself but also in the sensors monitoring the system, which makes the fault diagnosis task difficult, because the signals from which diagnostic reasoning takes place may be corrupted by faulty sensors. As such, many diagnosis solutions focus on either process or sensor faults, but not both. When considering both types of faults, additional diagnostic information is needed because of the additional ambiguity introduced by potentially faulted sensors. As such, traditional centralized diagnosis approaches, which already do not scale well, scale even worse. To address these issues, this paper presents a distributed diagnosis framework for physical systems applied to diagnosis of both sensor and process faults. Using a structural model decomposition method, we develop a distributed diagnoser design algorithm to build local fault diagnosers. These diagnosers are constructed based on global diagnosability analysis of the system, determining the minimal number of residuals required to have the maximum possible diagnosability in the system. We evaluate the design approach on a diagnostic benchmark system that is functionally representative of a spacecraft electrical power distribution system. Results demonstrate that the proposed distributed approach scales significantly better than a centralized approach.
  • M. Daigle, S. Sankararaman, and C. Kulkarni, "Stochastic Prediction of Remaining Driving Time and Distance for a Planetary Rover," 2015 IEEE Aerospace Conference, Big Sky, MT, March 2015. [show abstract]
    Abstract: The operations of a planetary rover depend critically upon the amount of power that can be delivered by its batteries. In order to plan the future operation of the rover, it is important to make reliable predictions regarding the end-of-discharge time, which, in turn, can be used to estimate the remaining driving time and distance of the rover. In addition, quantifying the uncertainty in these predictions is critical to making risk-informed decisions regarding the operations of the rover. This paper presents a computational methodology to stochastically predict end-of-discharge time, remaining driving time, and remaining driving distance for a planetary rover, based on monitoring the batteries that power the rover. We utilize a model-based prognostics framework that characterizes and incorporates the various sources of uncertainty into these predictions, thereby assisting operational decision-making. We consider two different types of driving scenarios, structured and unstructured driving, and characterize the uncertainty they create in the future usage of the rover. In structured driving, the rover navigates among a set of known waypoints, and in unstructured driving, the rover performs a sequence of unplanned maneuvers. Results from a set of field experiments illustrate these computational methods and demonstrate their applicability.
  • C. Kulkarni, M. Daigle, G. Gorospe, and K. Goebel, "Application of Model Based Prognostics to Pneumatic Valves in a Cryogenic Propellant Loading Testbed," AIAA SciTech Conference, Kissimmee, FL, January 2015. [show abstract]
    Abstract: Pneumatic-actuated valves are critical components in many applications, including cryogenic propellant loading for space operations. For these components, failures need to be predicted so that components can be repaired to ensure mission success, i.e., health monitoring and fault prognostics is required. In order to develop, test, mature, and deploy valve prognostics algorithms, we have developed a testbed for pneumatic valves used in cryogenic service for propellant loading operations, in which we can inject controlled damage profiles and observe its effects on valve operation. In this paper, we focus on the prognostics of a continuously-controlled pneumatic valve. We describe the construction of the testbed, the fault injection mechanisms, and the model-based valve prognostics algorithms. Experimental results from the testbed demonstrate successful prediction of valve failure.
2014
  • C. Kulkarni, M. Daigle, G. Gorospe, and K. Goebel, "Validation of Model-Based Prognostics for Pneumatic Valves in a Demonstration Testbed," Annual Conference of the Prognostics and Health Management Society 2014, pp. 76-85, Fort Worth, TX, September 2014. (nominated for best paper award) [show abstract]
    Abstract: Pneumatic-actuated valves play an important role in many applications, and for valves critical to the successful operation of the system, prognostics of these valves becomes extremely important and valuable. In order to facilitate the validation of prognostics algorithms for pneumatic valves, we have constructed a pneumatic valve testbed for use with a cryogenic propellant loading system. The testbed enables the injection of faults with a controllable fault progression profile. Specifically, we can introduce controllable pneumatic gas leaks, the most common faults associated with pneumatic valves. We focus on a valve that moves discretely between open and closed, and is controlled through a solenoid valve. In this paper, we apply a model-based prognostics approach for pneumatic valves on the testbed. We demonstrate the approach using real experimental data obtained from the testbed.
  • B. Bole, C. Kulkarni, and M. Daigle, "Adaptation of an Electrochemistry-based Li-Ion Battery Model to Account for Deterioration Observed Under Randomized Use," Annual Conference of the Prognostics and Health Management Society 2014, pp. 502-510, Fort Worth, TX, September 2014. [show abstract]
    Abstract: Tracking the variation in battery dynamics as a function of health is presently attracting attention in academia and industry due to the increased usage of expensive batteries in dynamic systems such as aircraft and electric cars. The online adaptation of battery models to account for age-dependent changes in dynamics is necessary to maintain accurate estimates of the remaining system operations that can be supported under battery power. A novel method for the adaptation of parameters in an electrochemical model of a lithium-ion battery is presented here. An unscented Kalman filtering algorithm is shown to enable the production of internal battery state estimates and age-dependent electrochemical model parameter estimates using only battery current and voltage data collected over randomized discharge profiles. The use of only data collected over randomized discharge profiles distinguishes this work from other works that make use of reference discharge cycles to judge battery health. The experimental results presented here compare online model estimates produced by the proposed algorithm to offline model estimates obtained by periodically taking batteries offline to run reference discharge cycles.
  • A. Sweet, G. Gorospe, M. Daigle, J. Celaya, E. Balaban, I. Roychoudhury, and S. Narasimhan, "Demonstration of Prognostics-Enabled Decision Making Algorithms on a Hardware Mobile Robot Test Platform," Annual Conference of the Prognostics and Health Management Society 2014, pp. 142-150, Fort Worth, TX, September 2014. [show abstract]
    Abstract: Prognostics-enabled Decision Making (PDM) is an emerging research area that aims to integrate prognostic health information and knowledge about the future operating conditions into the process of selecting subsequent actions for the system. Previous work developing and testing PDM algorithms has been done in simulation; this paper describes the effort leading to a successful demonstration of PDM algorithms on a hardware mobile robot platform. The hardware platform, based on the K11 planetary rover prototype, was modified to allow injection of selected fault modes related to the rover's electrical power subsystem. The PDM algorithms were adapted to the hardware platform, including development of a software module framework, a new route planner, and modifications to increase the algorithms' robustness to sensor noise and system timing issues. A set of test scenarios was chosen to demonstrate the algorithms' capabilities. The modifications to run with a hardware platform, the test scenarios, and the test results are described in detail. The results show a successful use of PDM algorithms on a hardware test platform to optimize mission planning in the presence of electrical system faults.
  • M. Daigle, I. Roychoudhury, and A. Bregon, "Qualitative Event-Based Fault Isolation under Uncertain Observations," Annual Conference of the Prognostics and Health Management Society 2014, pp. 347-355, Fort Worth, TX, September 2014. [show abstract]
    Abstract: For many systems, automatic fault diagnosis is critical to ensuring safe and efficient operation. Fault isolation is performed by analyzing measured signals from the system, and reasoning over the system behavior to determine which faults have occurred, based on models of predicted faulty behavior. For dynamic systems, reasoning may be performed using qualitative analysis of the differences between measured signals and their predicted values, in which observations take the form of qualitative symbols. Such an approach is quick to isolate faults, but depends critically on correct generation of the qualitative symbols from the signals. In this paper, we develop an approach to qualitative event-based fault isolation for dynamic systems that is robust to incorrect qualitative observations. Observations are treated as uncertain, where multiple interpretations of an observation, each with its own probability, are considered. By interpreting observed symbols in a probabilistic manner, the approach degrades gracefully as the number of incorrectly-generated symbols increases. The approach is demonstrated on an electrical power system testbed, and experiments using real data obtained from the hardware demonstrate the improved fault isolation performance in the presence of incorrect symbol generation.
  • M. Daigle, I. Roychoudhury, and A. Bregon, "Integrated Diagnostics and Prognostics for the Electrical Power System of a Planetary Rover," Annual Conference of the Prognostics and Health Management Society 2014, pp. 128-141, Fort Worth, TX, September 2014. [show abstract]
    Abstract: For electric vehicles, technology for monitoring, diagnosis, and prognosis of the electrical power system (EPS) becomes essential for safe and efficient operation. To this end, we develop a general system-level integrated diagnosis and prognosis framework, which detects, isolates, and identifies EPS faults, and predicts when the EPS will fail to deliver sufficient power. The approach takes advantage of recent work in structural model decomposition in order to distribute the global diagnosis and prognosis problems into local subproblems that can be solved in parallel, thus enabling implementation on distributed computational platforms. The framework is applied to the EPS of a planetary rover testbed, and is demonstrated using data from field experiments.
  • C. Kulkarni, G. Gorospe, M. Daigle, and K. Goebel, "A Testbed for Implementing Prognostic Methodologies on Cryogenic Propellant Loading Systems," 2014 IEEE Systems Readiness Technology Conference (AUTOTESTCON), pp. 280-289, St. Louis, MO, September 2014. (best paper award) [show abstract]
    Abstract: Prognostics technologies determine the health state of a system and predict its remaining useful life. With this information, operators are able to make maintenance-related decisions, thus effectively streamlining operational and mission-level activities. Experimentation on testbeds representative of critical systems is very useful for the maturation of prognostics technology; precise emulation of actual fault conditions on such a testbed further validates these technologies. In this paper we present the development of a pneumatic valve testbed, initial experimental results and progress towards the maturation and validation of component-level prognostic methods in the context of cryogenic refueling operations. The pneumatic valve testbed allows for the injection of time-varying leaks with specified damage progression profiles in order to emulate common valve faults. The pneumatic valve testbed also contains a battery used to power some pneumatic components, enabling the study of the effects of battery degradation on the operation of the valves.
  • M. Daigle, I. Roychoudhury, and A. Bregon, "Diagnosability-Based Sensor Placement through Structural Model Decomposition," Second European Conference of the Prognostics and Health Management Society 2014, pp. 33-46, Nantes, France, July 2014. [show abstract]
    Abstract: Systems health management, and in particular fault diagnosis, is important for ensuring safe, correct, and efficient operation of complex engineering systems. The performance of an online health monitoring system depends critically on the available sensors of the system. However, the set of selected sensors is subject to many constraints, such as cost and weight, and hence, these sensors must be selected judiciously. This paper presents an offline design-time sensor placement approach for complex systems. Our diagnosis method is built upon the analysis of model-based residuals, which are computed using structural model decomposition. Sensor placement in this framework manifests as a residual selection problem, and we aim to find the set of residuals that achieves single-fault diagnosability of the system, uses the minimum number of sensors, and corresponds to the best model decomposition for the best distribution of the diagnosis system. We present a set of algorithms for solving this problem and compare their performance in terms of computational complexity and optimality of solutions. We demonstrate the approach using a benchmark multi-tank system.
  • B. Bole, M. Daigle, and G. Gorospe, "Online Prediction of Battery Discharge and Estimation of Parasitic Loads for an Electric Aircraft," Second European Conference of the Prognostics and Health Management Society 2014, pp. 23-32, Nantes, France, July 2014. [show abstract]
    Abstract: Predicting whether or not vehicle batteries contain sufficient charge to support operations over the remainder of a given flight plan is critical for electric aircraft. This paper describes an approach for identifying upper and lower uncertainty bounds on predictions that aircraft batteries will continue to meet output power and voltage requirements over the remainder of a flight plan. Battery discharge prediction is considered here in terms of the following components; ($i$) online battery state of charge estimation; ($ii$) prediction of future battery power demand as a function of an aircraft flight plan; ($iii$) online estimation of additional parasitic battery loads; and finally, ($iv$) estimation of flight plan safety. Substantial uncertainty is considered to be an irremovable part of the battery discharge prediction problem. However, high-confidence estimates of flight plan safety or lack of safety are shown to be generated from even highly uncertain prognostic predictions.
  • S. Sankararaman, M. Daigle, and K. Goebel, "Uncertainty Quantification in Remaining Useful Life Prediction using First-Order Reliability Methods," IEEE Transactions on Reliability, vol. 63, no. 2, pp. 603-619, June 2014. [show abstract]
    Abstract: This paper investigates the use of first-order reliability methods to quantify the uncertainty in the remaining useful life (RUL) estimate of components used in engineering applications. The prediction of RUL is affected by several sources of uncertainty and it is important to systematically quantify their combined effect on the RUL prediction in order to aid risk assessment, risk mitigation, and decision-making. While sampling-based algorithms have been conventionally used for quantifying the uncertainty in RUL, analytical approaches are computationally cheaper, and sometimes, better suited for online decision-making. Exact analytical algorithms may not be available for practical engineering applications but effective approximations can be made using the first-order reliability methods. This paper describes three first-order reliability-based methods - first-order second moment method (FOSM), the first-order reliability method (FORM), and the inverse first-order reliability method (inverse-FORM) - for RUL uncertainty quantification. The inverse-FORM methodology is particularly useful in the context of online health monitoring, and this method is illustrated using the power system of an unmanned aerial vehicle, where the goal is to predict the end of discharge of a lithium-ion battery.
  • M. Daigle, A. Bregon, and I. Roychoudhury, "Distributed Prognostics Based on Structural Model Decomposition," IEEE Transactions on Reliability, vol. 63, no. 2, pp. 495-510, June 2014. [show abstract]
    Abstract: Within systems health management, prognostics focuses on predicting the remaining useful life of a system. In the model-based prognostics paradigm, physics-based models are constructed that describe the operation of a system, and how it fails. Such approaches consist of an estimation phase, in which the health state of the system is first identified, and a prediction phase, in which the health state is projected forward in time to determine the end of life. Centralized solutions to these problems are often computationally expensive, do not scale well as the size of the system grows, and introduce a single point of failure. In this paper, we propose a novel distributed model-based prognostics scheme that formally describes how to decompose both the estimation and prediction problems into computationally-independent local subproblems whose solutions may be easily composed into a global solution. The decomposition of the prognostics problem is achieved through structural decomposition of the underlying models. The decomposition algorithm creates from the global system model a set of local submodels suitable for prognostics. Computationally independent local estimation and prediction problems are formed based on these local submodels, resulting in a scalable distributed prognostics approach that allows the local subproblems to be solved in parallel, thus offering increases in computational efficiency. Using a centrifugal pump as a case study, we perform a number of simulation-based experiments to demonstrate the distributed approach, compare the performance with a centralized approach, and establish its scalability.
  • A. Bregon, M. Daigle, I. Roychoudhury, G. Biswas, X. Koutsoukos, and B. Pulido, "An Event-based Distributed Diagnosis Framework using Structural Model Decomposition," Artificial Intelligence, vol. 210, pp. 1-35, May 2014. [show abstract]
    Abstract: Complex engineering systems require efficient on-line fault diagnosis methodologies to improve safety and reduce maintenance costs. Traditionally, diagnosis approaches are centralized, but these solutions do not scale well. Also, centralized diagnosis solutions are difficult to implement on increasingly prevalent distributed, networked embedded systems. This paper presents a distributed diagnosis framework for physical systems with continuous behavior. Using Possible Conflicts, a structural model decomposition method from the Artificial Intelligence model-based diagnosis (DX) community, we develop a distributed diagnoser design algorithm to build local event-based diagnosers. These diagnosers are constructed based on global diagnosability analysis of the system, enabling them to generate local diagnosis results that are globally correct without the use of a centralized coordinator. We also use Possible Conflicts to design local parameter estimators that are integrated with the local diagnosers to form a comprehensive distributed diagnosis framework. Hence, this is a fully distributed approach to fault detection, isolation, and identification. We evaluate the developed scheme on a four-wheeled rover for different design scenarios to show the advantages of using Possible Conflicts, and generate on-line diagnosis results in simulation to demonstrate the approach.
  • M. Daigle, C. Kulkarni, and G. Gorospe, "Application of Model-based Prognostics to a Pneumatic Valves Testbed," 2014 IEEE Aerospace Conference, Big Sky, MT, March 2014. [show abstract]
    Abstract: Pneumatic-actuated valves play an important role in many applications, including cryogenic propellant loading for space operations. Model-based prognostics emphasizes the importance of a model that describes the nominal and faulty behavior of a system, and how faulty behavior progresses in time, causing the end of useful life of the system. We describe the construction of a testbed consisting of a pneumatic valve that allows the injection of faulty behavior and controllable fault progression. The valve opens discretely, and is controlled through a solenoid valve. Controllable leaks of pneumatic gas in the testbed are introduced through proportional valves, allowing the testing and validation of prognostics algorithms for pneumatic valves. A new valve prognostics approach is developed that estimates fault progression and predicts remaining life based only on valve timing measurements. Simulation experiments demonstrate and validate the approach.
  • M. Daigle and C. Kulkarni, "A Battery Health Monitoring Framework for Planetary Rovers," 2014 IEEE Aerospace Conference, Big Sky, MT, March 2014. (best paper award) [show abstract]
    Abstract: Batteries have seen an increased use in electric ground and air vehicles for commercial, military, and space applications as the primary energy source. An important aspect of using batteries in such contexts is battery health monitoring. Batteries must be carefully monitored such that the battery health can be determined, and end of discharge and end of usable life events may be accurately predicted. For planetary rovers, battery health estimation and prediction is critical to mission planning and decision-making. We develop a model-based approach utilizing computationally efficient and accurate electrochemistry models of batteries. An unscented Kalman filter yields state estimates, which are then used to predict the future behavior of the batteries and, specifically, end of discharge. The prediction algorithm accounts for possible future power demands on the rover batteries in order to provide meaningful results and an accurate representation of prediction uncertainty. The framework is demonstrated on a set of lithium-ion batteries powering a rover at NASA Ames Research Center using real experimental field test data.
  • C. Teubert and M. Daigle, "Current/Pressure Transducer Application of Model-Based Prognostics using Steady State Conditions," 2014 IEEE Aerospace Conference, Big Sky, MT, March 2014. [show abstract]
    Abstract: Prognostics is the process of predicting a system's future states, health degradation/wear, and remaining useful life (RUL). This information plays an important role in preventing failure, reducing downtime, scheduling maintenance, and improving system utility. Prognostics relies heavily on wear estimation. In some components, the sensors used to estimate wear may not be fast enough to capture brief transient states that are indicative of wear. For this reason it is beneficial to be capable of detecting and estimating the extent of component wear using steady-state measurements. This paper details a method for estimating component wear using steady-state measurements, describes how this is used to predict future states, and presents a case study of a current/pressure (I/P) Transducer. I/P Transducer nominal and off-nominal behaviors are characterized using a physics-based model, and validated against expected and observed component behavior. This model is used to map observed steady-state responses to corresponding fault parameter values in the form of a lookup table. This method was chosen because of its fast, efficient nature, and its ability to be applied to both linear and non-linear systems. Using measurements of the steady state output, and the lookup table, wear is estimated. A regression is used to estimate the wear propagation parameter and characterize the damage progression function, which are used to predict future states and the remaining useful life of the system.
2013
  • M. Daigle, I. Roychoudhury, and A. Bregon, "Qualitative Event-based Diagnosis with Possible Conflicts: Case Study on the Fourth International Diagnostic Competition," Proceedings of the 24th International Workshop on Principles of Diagnosis, pp. 230-235, Jerusalem, Israel, October 2013. [show abstract]
    Abstract: The objective of the International Diagnostic Competition is to provide a platform for evaluating how different diagnostic algorithms perform and compare to one another when applied to the same problem. This paper describes three model-based diagnosis algorithms entered into the Fourth International Diagnostic Competition. We focus on the first diagnostic problem of the industrial track of the competition in which a diagnosis algorithm must detect, isolate, and identify faults in an electrical power distribution testbed in order to provide abort recommendations when warranted. We present here a general fault isolation framework that encompasses three algorithms, each of which use different residual sets for fault isolation; one based on the global system model, one based on minimal submodels computed using Possible Conflicts, and one based on the combination of the former two residual sets. We describe, compare, and contrast the three algorithms in terms of practical implementation and their diagnosis results.
  • A. Sweet, A. Feldman, S. Narasimhan, M. Daigle, and S. Poll, "Fourth International Diagnostic Competition - DXC'13," Proceedings of the 24th International Workshop on Principles of Diagnosis, pp. 224-229, Jerusalem, Israel, October 2013. [show abstract]
    Abstract: We present the description and results of the Fourth International Diagnostic Competition, which tests and evaluates diagnostic algorithms (DAs). This year's competition featured the ad-dition of a thermal fluid track, in which the DAs perform diagnosis of a building's heating, ven-tilation, and air-conditioning (HVAC) system. The previous industrial track competition was also held, with a total of 5 DAs participating. The paper briefly reviews the industrial track used in this and previous competitions, and de-scribes the new thermal fluid track with its sys-tem in detail. The participating DAs in each track are described, and the scoring metrics and competition results are presented.
  • C. Quach, B. Bole, E. Hogge, S. Vazquez, M. Daigle, J. Celaya, A. Weber, and K. Goebel, "Battery Charge Depletion Prediction on an Electric Aircraft," Annual Conference of the Prognostics and Health Management Society 2013, pp. 503-512, New Orleans, LA, October 2013. [show abstract]
    Abstract: Validation of prognostic technologies through ground and flight tests is an important step in maturing these novel technologies and deploying them on real-world systems. To this end, a series of flight tests have been conducted using an unmanned electric vehicle during which the motor system batteries were monitored by a prognostic algorithm. The research presented here endeavors to produce and validate a technology for predicting the remaining time until end-of discharge of the batteries on an electric aircraft as a function of an expected future flight and online estimates of the charge contained in the batteries. Flight data and flight experiment results are presented along with an assessment of model and algorithm performance.
  • J. Barber, K. Johnston, and M. Daigle, "A Cryogenic Fluid System Simulation in Support of Integrated Systems Health Management," Annual Conference of the Prognostics and Health Management Society 2013, pp. 114-126, New Orleans, LA, October 2013. [show abstract]
    Abstract: Simulations serve as important tools throughout the design and operation of engineering systems. In the context of systems health management, simulations serve many uses. For one, the underlying physical models can be used by model-based health management tools to develop diagnostic and prognostic models. These simulations should incorporate both nominal and faulty behavior with the ability to inject various faults into the system. Such simulations can therefore be used for operator training, for both nominal and faulty situations, as well as for developing and prototyping health management algorithms. In this paper, we describe a methodology for building such simulations. We discuss the design decisions and tools used to build a simulation of a cryogenic fluid test bed, and how it serves as a core technology for systems health management development and maturation.
  • C. Teubert and M. Daigle, "I/P Transducer Application of Model-Based Wear Detection and Estimation using Steady State Conditions," Annual Conference of the Prognostics and Health Management Society 2013, pp. 134-140, New Orleans, LA, October 2013. [show abstract]
    Abstract: For modern systems, wear estimation plays an important role in preventing failure, scheduling maintenance, and improving utility. Wear estimation relies on a series of sensors, measuring the state of the system. In some components, the sensors used to estimate wear may not be fast enough to capture brief transient states that are indicative of wear. For this reason it is beneficial to be capable of detecting and estimating the extent of component wear using steady-state measurements. This paper details a method for estimating component wear using steady-state measurements, and describes a case study of a current/pressure (I/P) transducer. I/P Transducer nominal and off-nominal behavior are characterized using a physics-based model, and validated against expected component behavior. This model is used to determine steady state responses to many common I/P Transducer wear modes, isolate the active wear mode, and estimate its magnitude.
  • M. Daigle and C. Kulkarni, "Electrochemistry-based Battery Modeling for Prognostics," Annual Conference of the Prognostics and Health Management Society 2013, pp. 249-261, New Orleans, LA, October 2013. (nominated for best paper award) [show abstract]
    Abstract: Batteries are used in a wide variety of applications. In recent years, they have become popular as a source of power for electric vehicles such as cars, unmanned aerial vehicles, and commericial passenger aircraft. In such application domains, it becomes crucial to both monitor battery health and performance and to predict end of discharge (EOD) and end of useful life (EOL) events. To implement such technologies, it is crucial to understand how batteries work and to capture that knowledge in the form of models that can be used by monitoring, diagnosis, and prognosis algorithms. In this work, we develop electrochemistry-based models of lithium-ion batteries that capture the significant electrochemical processes, are computationally efficient, capture the effects of aging, and are of suitable accuracy for reliable EOD prediction in a variety of usage profiles. This paper reports on the progress of such a model, with results demonstrating the model validity and accurate EOD predictions.
  • M. Daigle and S. Sankararaman, "Advanced Methods for Determining Prediction Uncertainty in Model-Based Prognostics with Application to Planetary Rovers," Annual Conference of the Prognostics and Health Management Society 2013, pp. 262-274, New Orleans, LA, October 2013. (nominated for best paper award) [show abstract]
    Abstract: Prognostics is centered on predicting the time of and time until adverse events in components, subsystems, and systems. It typically involves both a state estimation phase, in which the current health state of a system is identified, and a prediction phase, in which the state is projected forward in time. Since prognostics is mainly a prediction problem, prognostic approaches cannot avoid uncertainty, which arises due to several sources. Prognostics algorithms must both characterize this uncertainty and propagate it through the predictions so that informed decisions can be made about the system. In this paper, we describe methods to solve these problems, including Monte Carlo-, unscented transform-, and first-order reliability-based methods. Using a planetary rover as a case study, we demonstrate and compare the different methods in simulation for battery end-of-discharge prediction.
  • A. Bregon, S. Narasimhan, I. Roychoudhury, M. Daigle, and B. Pulido, "An Efficient Model-based Diagnosis Engine for Hybrid Systems using Structural Model Decomposition," Annual Conference of the Prognostics and Health Management Society 2013, pp. 312-324, New Orleans, LA, October 2013. [show abstract]
    Abstract: Complex hybrid systems are present in a large range of engineering applications, like mechanical systems, electrical circuits, and embedded computation systems. The behavior of these systems is made up of continuous and discrete event dynamics that increase the difficulties for accurate and timely online fault diagnosis. The Hybrid Diagnosis Engine (HyDE) architecture offers flexibility to the diagnosis application designer to choose the modeling paradigm and the reasoning algorithms. The HyDE architecture supports the use of multiple modeling paradigms at the component and system level. However, HyDE faces some problems regarding performance in terms of time and space complexity. This paper focuses on developing efficient model-based methodologies for online fault diagnosis in complex hybrid systems. To do this, we propose a diagnosis framework where structural model decomposition is integrated within the HyDE diagnosis framework to reduce the computational complexity associated with the fault diagnosis of hybrid systems. As a case study, we apply our approach to a diagnostic benchmark problem, the Advanced Diagnostics and Prognostics Testbed (ADAPT), using real data.
  • C. Kulkarni, M. Daigle, and K. Goebel, "Implementation of Prognostic Methodologies to Cryogenic Propellant Loading Testbed," 2013 IEEE Systems Readiness Technology Conference (AUTOTESTCON), pp. 314-320, Schaumburg, IL, September 2013. [show abstract]
    Abstract: Prognostics methodologies determine the health state of a system and predict the end of life and remaining useful life. This information enables operators to take maintenance-related decisions, thus effectively streamlining operational and mission-level activities. Prognostics testbeds help in the prototyping, development, and maturation of prognostic technologies. In this work, we present a prognostics testbed for pneumatic valves. Pneumatic valves are critical components in many industrial processes, and the testbed will be used to showcase how remaining life prediction works in the context of cryogenic refueling operations. The testbed allows for the injection of time-varying leaks with specified damage progression profiles in order to emulate common valve faults. In addition, the testbed contains a battery used to power some components, allowing the study of the effects of battery degradation on the operation of the valves. Prognostic algorithms will utilize sensor data collected from the different transducers in order to estimate component health and make life predictions, based on mathematical models describing the underlying physics of component degradation and employing a Bayesian filtering algorithm for state-parameter estimation from which life predictions are made.
  • E. Balaban, S. Narasimhan, M. Daigle, I. Roychoudhury, A. Sweet, C. Bond, and G. Gorospe, "Development of a Mobile Robot Test Platform and Methods for Validation of Prognostics-Enabled Decision Making Algorithms," International Journal of Prognostics and Health Management, vol. 4, no. 1, May 2013. [show abstract]
    Abstract: As fault diagnosis and prognosis systems in aerospace applications become more capable, the ability to utilize information supplied by them becomes increasingly important. While certain types of vehicle health data can be effectively processed and acted upon by crew or support personnel, others, due to their complexity or time constraints, require either automated or semi-automated reasoning. Prognostics-enabled Decision Making (PDM) is an emerging research area that aims to integrate prognostic health information and knowledge about the future operating conditions into the process of selecting subsequent actions for the system. The newly developed PDM algorithms require suitable software and hardware platforms for testing under realistic fault scenarios. The paper describes the development of such a platform, based on the K11 planetary rover prototype. A variety of injectable fault modes is being investigated for electrical, mechanical, and power subsystems of the testbed, along with methods for data collection and processing. In addition to the hardware platform, a software simulator with matching capabilities has been developed. The simulator allows for prototyping and initial validation of the algorithms prior to their deployment on the K11. The simulator is also available to the PDM algorithms to assist with the reasoning process. A reference set of diagnostic, prognostic, and decision making algorithms is also described, followed by an overview of the current test scenarios and the results of their execution on the simulator.
  • M. Daigle and K. Goebel, "Model-based Prognostics with Concurrent Damage Progression Processes," IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 43, no. 4, pp. 535-546, May 2013. [show abstract]
    Abstract: Model-based prognostics approaches rely on physics-based models that describe the behavior of systems and their components. These models must account for the several different damage processes occurring simultaneously within a component. Each of these damage and wear processes contribute to the overall component degradation. We develop a model-based prognostics methodology that consists of a joint state-parameter estimation problem, in which the state of a system along with parameters describing the damage progression are estimated, followed by a prediction problem, in which the joint state-parameter estimate is propagated forward in time to predict end of life and remaining useful life. The state-parameter estimate is computed using a particle filter, and is represented as a probability distribution, allowing the prediction of end of life and remaining useful life within a probabilistic framework that supports uncertainty management. We also develop a novel variance control algorithm that maintains an uncertainty bound around the unknown parameters to limit the amount of estimation uncertainty and, consequently, reduce prediction uncertainty. We construct a detailed physics-based model of a centrifugal pump that includes damage progression models, to which we apply our model-based prognostics algorithm. We illustrate the operation of the prognostic solution with a number of simulation-based experiments and demonstrate the performance of the approach when multiple damage mechanisms are active.
  • I. Roychoudhury, M. Daigle, A. Bregon, and B. Pulido, "A Structural Model Decomposition Framework for Systems Health Management," 2013 IEEE Aerospace Conference, Big Sky, MT, March 2013. [show abstract]
    Abstract: Systems health management (SHM) is an important set of technologies aimed at increasing system safety and reliability by detecting, isolating, and identifying faults; and predicting when the system reaches end of life (EOL), so that appropriate fault mitigation and recovery actions can be taken. Model-based SHM approaches typically make use of global, monolithic system models for online analysis, which results in a loss of scalability and efficiency for large-scale systems. Improvement in scalability and efficiency can be achieved by decomposing the system model into smaller local submodels and operating on these submodels instead. In this paper, the global system model is analyzed offline and structurally decomposed into local submodels. We define a common model decomposition framework for extracting submodels from the global model. This framework is then used to develop algorithms for solving model decomposition problems for the design of three separate SHM technologies, namely, estimation (which is useful for fault detection and identification), fault isolation, and EOL prediction. We solve these model decomposition problems using a three-tank system as a case study.
  • S. Sankararaman, M. Daigle, A. Saxena, and K. Goebel, "Analytical Algorithms to Quantify the Uncertainty in Remaining Useful Life Prediction," 2013 IEEE Aerospace Conference, Big Sky, MT, March 2013. [show abstract]
    Abstract: This paper investigates the use of analytical algorithms to quantify the uncertainty in the remaining useful life (RUL) estimate of components used in aerospace applications. The prediction of RUL is affected by several sources of uncertainty and it is important to systematically quantify their combined effect by computing the uncertainty in the RUL prediction in order to aid risk assessment, risk mitigation, and decision-making. While sampling-based algorithms have been conventionally used for quantifying the uncertainty in RUL, analytical algorithms are computationally cheaper and sometimes, are better suited for online decision-making. While exact analytical algorithms are available only for certain special cases (for e.g., linear models with Gaussian variables), effective approxima tions can be made using the first-order second moment method (FOSM), the first-order reliabilitymethod (FORM), and the inverse first-order reliabilitymethod (Inverse FORM). These methods can be used not only to calculate the entire probability distribution of RUL but also to obtain probability bounds on RUL. This paper explains these three methods in detail and illustrates them using the state-space model of a lithium-ion battery.
  • M. Daigle, J. Boschee, M. Foygel, and V. Smelyanskiy, "Temperature Stratification in a Cryogenic Fuel Tank," AIAA Journal of Thermophysics and Heat Transfer, vol. 27, no. 1, pp. 116-126, January 2013. [show abstract]
    Abstract: A reduced dynamical model describing temperature stratification effects driven by natural convection in a liquid hydrogen cryogenic fuel tank has been developed. It accounts for cryogenic propellant loading, storage, and unloading in the conditions of normal, increased, and micro- gravity. The model involves multiple horizontal control volumes in both liquid and ullage spaces. Temperature and velocity boundary layers at the tank walls are taken into account by using correlation relations. Heat exchange involving the tank wall is considered by means of the lumped-parameter method. By employing basic conservation laws, the model takes into consideration the major multi-phase mass and energy exchange processes involved, such as condensation-evaporation of the hydrogen, as well as flows of hydrogen liquid and vapor in the presence of pressurizing helium gas. The model involves a liquid hydrogen feed line and a tank ullage vent valve for pressure control. The temperature stratification effects are investigated, including in the presence of vent valve oscillations. A simulation of temperature stratification effects in a generic cryogenic tank has been implemented in Matlab and results are presented for various tank conditions.
2012
  • M. Daigle, A. Bregon, and I. Roychoudhury, "A Distributed Approach to System-Level Prognostics," Annual Conference of the Prognostics and Health Management Society 2012, pp. 71-82, Minneapolis, MN, September 2012. [show abstract]
    Abstract: Prognostics, which deals with predicting remaining useful life of components, subsystems, and systems, is a key technology for systems health management that leads to improved safety and reliability with reduced costs. The prognostics problem is often approached from a component-centric view. However, in most cases, it is not specifically component lifetimes that are important, but, rather, the lifetimes of the systems in which these components reside. The system-level prognostics problem can be quite difficult due to the increased scale and scope of the prognostics problem and the relative lack of scalability and efficiency of typical prognostics approaches. In order to address these issues, we develop a distributed solution to the system-level prognostics problem, based on the concept of structural model decomposition. The system model is decomposed into independent submodels. Independent local prognostics subproblems are then formed based on these local submodels, resulting in a scalable, efficient, and flexible distributed approach to the system-level prognostics problem. We provide a formulation of the system-level prognostics problem and demonstrate the approach on a four-wheeled rover simulation testbed. The results show that the system-level prognostics problem can be accurately and efficiently solved in a distributed fashion.
  • M. Daigle, A. Saxena, and K. Goebel, "An Efficient Deterministic Approach to Model-based Prediction Uncertainty Estimation," Annual Conference of the Prognostics and Health Management Society 2012, pp. 326-335, Minneapolis, MN, September 2012. [show abstract]
    Abstract: Prognostics deals with the prediction of the end of life (EOL) of a system. EOL is a random variable, due to the presence of process noise and uncertainty in the future inputs to the system. Prognostics algorithms must account for this inherent uncertainty. In addition, these algorithms never know exactly the state of the system at the desired time of prediction, or the exact model describing the future evolution of the system, accumulating additional uncertainty into the predicted EOL. Prediction algorithms that do not account for these sources of uncertainty are misrepresenting the EOL and can lead to poor decisions based on their results. In this paper, we explore the impact of uncertainty in the prediction problem. We develop a general model-based prediction algorithm that incorporates these sources of uncertainty, and propose a novel approach to efficiently handle uncertainty in the future input trajectories of a system by using the unscented transform. Using this approach, we are not only able to reduce the computational load but also estimate the bounds of uncertainty in a deterministic manner, which can be useful to consider during decision-making. Using a lithium-ion battery as a case study, we perform several simulation-based experiments to explore these issues, and validate the overall approach using experimental data from a battery testbed.
  • A. Bregon, M. Daigle, and I. Roychoudhury, "An Integrated Framework for Model-based Distributed Diagnosis and Prognosis," Annual Conference of the Prognostics and Health Management Society 2012, pp. 416-426, Minneapolis, MN, September 2012. [show abstract]
    Abstract: Diagnosis and prognosis are necessary tasks for system reconfiguration and fault-adaptive control in complex systems. Diagnosis consists of detection, isolation and identification of faults, while prognosis consists of prediction of the remaining useful life of systems. This paper presents a novel integrated framework for model-based distributed diagnosis and prognosis, where system decomposition is used to enable the diagnosis and prognosis tasks to be performed in a distributed way. We show how different submodels can be automatically constructed to solve the local diagnosis and prognosis problems. We illustrate our approach using a simulated four-wheeled rover for different fault scenarios. Our experiments show that our approach correctly performs distributed fault diagnosis and prognosis in an efficient and robust manner.
  • S. Narasimhan, E. Balaban, M. Daigle, I. Roychoudhury, A. Sweet, J. Celaya, and K. Goebel, "Autonomous Decision Making for Planetary Rovers Using Diagnostic and Prognostic Information," 8th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes, pp. 289-294, Mexico City, Mexico, August 2012. [show abstract]
    Abstract: Rover missions typically involve visiting a set of predetermined waypoints to perform science functions, such as sample collection. Given the communication delay between Earth and the rover, and the possible occurrence of faults, an autonomous decision making system is essential to ensure that the rover maximizes the scientific operations performed without damaging itself further or stalling. This paper presents a modular software architecture for autonomous decision making for rover operations that uses diagnostic and prognostic information to influence mission planning and decision making to maximize the completion of mission objectives. The decision making system consists of separate modules that perform the functions of control, diagnosis, prognosis, and decision making. We demonstrate our implementation of this architecture on a simulated rover testbed.
  • M. Daigle, A. Bregon, G. Biswas, X. Koutsoukos, and B. Pulido, "Improving Multiple Fault Diagnosability using Possible Conflicts," 8th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes, pp. 144-149, Mexico City, Mexico, August 2012. [show abstract]
    Abstract: Multiple fault diagnosis is a difficult problem for dynamic systems. Due to fault masking, compensation, and relative time of fault occurrence, multiple faults can manifest in many different ways as observable fault signature sequences. This decreases diagnosability of multiple faults, and therefore leads to a loss in effectiveness of the fault isolation step. We develop a qualitative, event-based, multiple fault isolation framework, and derive several notions of multiple fault diagnosability. We show that using Possible Conflicts, a model decomposition technique that decouples faults from residuals, we can significantly improve the diagnosability of multiple faults compared to an approach using a single global model. We demonstrate these concepts and provide results using a multi-tank system as a case study.
  • M. Daigle, A. Bregon, and I. Roychoudhury, "Qualitative Event-based Diagnosis with Possible Conflicts Applied to Spacecraft Power Distribution Systems," 8th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes, pp. 265-270, Mexico City, Mexico, August 2012. [show abstract]
    Abstract: Model-based diagnosis enables efficient and safe operation of engineered systems. In this paper, we describe two algorithms based on a qualitative event-based fault isolation framework augmented with model-based fault identification that are applied to spacecraft power distribution systems. Although based on a common framework, the fundamental difference between the two algorithms is that one uses a global model for residual generation, fault isolation, and fault identification; whereas the other uses a set of minimal submodels computed using Possible Conflicts. We describe the implementation of the two algorithms and compare their diagnosis results on a representative spacecraft power distribution system.
  • A. Bregon, M. Daigle, and I. Roychoudhury, "An Integrated Model-Based Distributed Diagnosis and Prognosis Framework," Proceedings of the 23rd International Workshop on Principles of Diagnosis, pp. 3-10, Great Malvern, U.K., July 2012. [show abstract]
    Abstract: Diagnosis and prognosis are necessary tasks for system reconfiguration and fault-adaptive control in complex systems. Diagnosis consists of detection, isolation and identification of faults, while prognosis consists of prediction of the remaining useful life of systems. This paper presents an integrated model-based distributed diagnosis and prognosis framework, where system decomposition is used to perform the diagnosis and prognosis tasks in a distributed way. We show how different submodels can be automatically constructed to solve the local diagnosis and prognosis problems. We illustrate our approach using a simulated four-wheeled rover for different fault scenarios. Our experiments show that our approach correctly performs fault diagnosis and prognosis in a robust manner.
  • M. Daigle, B. Saha, and K. Goebel, "A Comparison of Filter-based Approaches for Model-based Prognostics," 2012 IEEE Aerospace Conference, Big Sky, MT, March 2012. [show abstract]
    Abstract: Model-based prognostics approaches use domain knowledge about a system and its failure modes through the use of physics-based models. Model-based prognosis is generally divided into two sequential problems: a joint state-parameter estimation problem, in which, using the model, the health of a system or component is determined based on the observations; and a prediction problem, in which, using the model, the state-parameter distribution is simulated forward in time to compute end of life and remaining useful life. The first problem is typically solved through the use of a state observer, or filter. The choice of filter depends on the assumptions that may be made about the system, and on the desired algorithm performance. In this paper, we review three separate filters for the solution to the first problem: the Daum filter, an exact nonlinear filter; the unscented Kalman filter, which approximates nonlinearities through the use of a deterministic sampling method known as the unscented transform; and the particle filter, which approximates the state distribution using a finite set of discrete, weighted samples, called particles. Using a centrifugal pump as a case study, we conduct a number of simulation-based experiments investigating the performance of the different algorithms as applied to prognostics.
2011
  • V. Osipov, M. Daigle, C. Muratov, M. Foygel, V. Smelyanskiy, and M. Watson, Dynamical Model of Rocket Propellant Loading with Liquid Hydrogen," AIAA Journal of Spacecraft and Rockets, vol. 48, no. 6, pp. 987-998, November 2011. [show abstract]
    Abstract: A dynamical model describing the multi-stage process of rocket propellant loading has been developed. It accounts for both the nominal and faulty regimes of cryogenic fuel loading when liquid hydrogen is moved from a storage tank to an external tank via a transfer line. By employing basic conservation laws, the reduced, lumped-parameter model takes into consideration the major multi-phase mass and energy exchange processes involved, such as highly non-equilibrium condensation-evaporation of hydrogen, pressurization of the tanks, and liquid hydrogen and hydrogen vapor flows in the presence of pressurizing helium gas. A self-consistent theory of dynamical condensation-evaporation has been developed that incorporates heat flow by both conduction and convection through the liquid-vapor interface inside the tanks. A simulation has been developed in Matlab for a generic refueling system that involves the solution of a system of ordinary integro-differential equations. The results of these simulations are in good agreement with the Space Shuttle refueling data.
  • S. Poll, J. de Kleer, R. Abreau, M. Daigle, A. Feldman, D. Garcia, A. Gonzalez-Sanchez, T. Kurtoglu, S. Narasimhan, and A. Sweet, "Third International Diagnostic Competition - DXC'11," Proceedings of the 22nd International Workshop on Principles of Diagnosis, pp. 267-278, Murnau, Germany, October 2011. [show abstract]
    Abstract: We present the third implementation of a framework created jointly by NASA Ames Research Center, Palo Alto Research Center, and Delft University of Technology to compare and evaluate diagnosis algorithms (DAs). This year's competition, DXC'11, introduces a software track in addition to the industrial and synthetic tracks of previous competitions. A total of eleven DAs competed in the three tracks. The paper describes the systems, diagnostic problems of the tracks, fault scenarios, evaluation metrics, participating DAs, results and analysis.
  • M. Daigle, A. Bregon, and I. Roychoudhury, "Qualitative Event-based Diagnosis with Possible Conflicts: Case Study on the Third International Diagnostic Competition," Proceedings of the 22nd International Workshop on Principles of Diagnosis, pp. 285-292, Murnau, Germany, October 2011. [show abstract]
    Abstract: We describe two model-based diagnosis algorithms entered into the Third International Diagnostic Competition. We focus on the first diagnostic problem of the industrial track of the competition in which a diagnosis algorithm must detect, isolate, and identify faults in an electrical power distribution testbed in order to provide correct abort recommendations. Both diagnosis algorithms are based on a qualitative event-based fault isolation framework augmented with model-based fault identification. Although based on a common framework, the fundamental difference between the two algorithms is that one is based on a global model for residual generation, fault isolation, and fault identification, whereas the other uses a set of minimal submodels computed using Possible Conflicts. We describe, compare, and contrast the two algorithms in terms of practical implementation and their diagnosis results.
  • A. Bregon, M. Daigle, I. Roychoudhury, G. Biswas, X. Koutsoukos, and B. Pulido, "Improving Distributed Diagnosis Through Structural Model Decomposition," Proceedings of the 22nd International Workshop on Principles of Diagnosis, pp. 195-202, Murnau, Germany, October 2011. [show abstract]
    Abstract: Complex engineering systems require efficient fault diagnosis methodologies, but centralized approaches do not scale well, and this motivates the development of distributed solutions. This work presents an event-based approach for distributed diagnosis of abrupt parametric faults in continuous systems, by using the structural model decomposition capabilities provided by Possible Conflicts. We develop a distributed diagnosis algorithm that uses residuals, computed by extending Possible Conflicts, to build local event-based diagnosers based on global diagnosability analysis that generate globally correct local diagnosis results. The proposed approach is applied to a multi-tank system, and results demonstrate an improvement in the design of local diagnosers. Since local diagnosers use only a subset of the residuals, and use subsystem models to compute residuals (instead of the global system model), the local diagnosers are more efficient than previously developed distributed approaches.
  • I. Roychoudhury and M. Daigle, "An Integrated Model-Based Diagnostic and Prognostic Framework," Proceedings of the 22nd International Workshop on Principles of Diagnosis, pp. 44-51, Murnau, Germany, October 2011. [show abstract]
    Abstract: Systems health monitoring is essential in guaranteeing the safe, efficient, and correct operation of complex engineered systems. Diagnosis, which consists of detection, isolation and identification of faults; and prognosis, which consists of prediction of the remaining useful life of components, subsystems, or systems; constitute systems health monitoring. This paper presents an integrated model-based diagnostic and prognostic framework, where we make use of a common modeling paradigm to model both the nominal and faulty behavior in all aspects of systems health monitoring. We illustrate our approach using a simulated propellant loading system that includes tanks, valves, and pumps.
  • M. Daigle, A. Bregon, and I. Roychoudhury, "Distributed Damage Estimation for Prognostics Based on Structural Model Decomposition," Proceedings of the Annual Conference of the Prognostics and Health Management Society 2011, pp. 198-208, Montreal, Quebec, Canada, September 2011. (nominated for best paper award) [show abstract]
    Abstract: Model-based prognostics approaches capture system knowledge in the form of physics-based models of components that include how they fail. These methods consist of a damage estimation phase, in which the health state of a component is estimated, and a prediction phase, in which the health state is projected forward in time to determine end of life. However, the damage estimation problem is often multi-dimensional and computationally intensive. We propose a model decomposition approach adapted from the diagnosis community, called possible conflicts, in order to both improve the computational efficiency of damage estimation, and formulate a damage estimation approach that is inherently distributed. Local state estimates are combined into a global state estimate from which prediction is performed. Using a centrifugal pump as a case study, we perform a number of simulation-based experiments to demonstrate the approach.
  • M. Daigle, I. Roychoudhury, S. Narasimhan, S. Saha, B. Saha, and K. Goebel, "Investigating the Effect of Damage Progression Model Choice on Prognostics Performance," Proceedings of the Annual Conference of the Prognostics and Health Management Society 2011, pp. 323-333, Montreal, Quebec, Canada, September 2011. (won best paper award - theory paper) [show abstract]
    Abstract: The success of model-based approaches to systems health management depends largely on the quality of the underlying models. In model-based prognostics, it is especially the quality of the damage progression models, i.e., the models describing how damage evolves as the system operates, that determines the accuracy and precision of remaining useful life predictions. Several common forms of these models are generally assumed in the literature, but are often not supported by physical evidence or physics-based analysis. In this paper, using a centrifugal pump as a case study, we develop different damage progression models. In simulation, we investigate how model changes influence prognostics performance. Results demonstrate that, in some cases, simple damage progression models are sufficient. But, in general, the results show a clear need for damage progression models that are accurate over long time horizons under varied loading conditions.
  • E. Balaban, S. Narasimhan, M. Daigle, J. Celaya, I. Roychoudhury, B. Saha, S. Saha, and K. Goebel, "A Mobile Robot Testbed for Prognostics-Enabled Autonomous Decision Making," Proceedings of the Annual Conference of the Prognostics and Health Management Society 2011, pp. 15-30, Montreal, Quebec, Canada, September 2011. (nominated for best paper award) [show abstract]
    Abstract: The ability to utilize prognostic system health information in operational decision making, especially when fused with information about future operational, environmental, and mission requirements, is becoming desirable for both manned and unmanned aerospace vehicles. A vehicle capable of evaluating its own health state and making (or assisting the crew in making) decisions with respect to its system health evolution over time will be able to go further and accomplish more mission objectives than a vehicle fully dependent on human control. This paper describes the development of a hardware testbed for integration and testing of prognostics-enabled decision making technologies. Although the testbed is based on a planetary rover platform (K11), the algorithms being developed on it are expected to be applicable to a variety of aerospace vehicle types, from unmanned aerial vehicles and deep space probes to manned aircraft and spacecraft. A variety of injectable fault modes is being investigated for electrical, mechanical, and power subsystems of the testbed. A software simulator of the K11 has been developed, for both nominal and off-nominal operating modes, which allows prototyping and validation of algorithms prior to their deployment on hardware. The simulator can also aid in the decision-making process. The testbed is designed to have interfaces that allow reasoning software to be integrated and tested quickly, making it possible to evaluate and compare algorithms of various types and from different sources. Currently, algorithms developed (or being developed) at NASA Ames - a diagnostic system, a prognostic system, a decision-making module, a planner, and an executive - are being used to complete the software architecture and validate design of the testbed.
  • M. Daigle and K. Goebel, "A Model-based Prognostics Approach Applied to Pneumatic Valves," International Journal of Prognostics and Health Management, vol. 2, no. 2, August 2011. [show abstract]
    Abstract: Within the area of systems health management, the task of prognostics centers on predicting when components will fail. Model-based prognostics exploits domain knowledge of the system, its components, and how they fail by casting the underlying physical phenomena in a physics-based model that is derived from first principles. Uncertainty cannot be avoided in prediction, therefore, algorithms are employed that help in managing these uncertainties. The particle filtering algorithm has become a popular choice for model-based prognostics due to its wide applicability, ease of implementation, and support for uncertainty management. We develop a general model-based prognostics methodology within a robust probabilistic framework using particle filters. As a case study, we consider a pneumatic valve from the Space Shuttle cryogenic refueling system. We develop a detailed physics-based model of the pneumatic valve, and perform comprehensive simulation experiments to illustrate our prognostics approach and evaluate its effectiveness and robustness. The approach is demonstrated using historical pneumatic valve data from the refueling system.
  • I. Roychoudhury, M. Daigle, G. Biswas, and X. Koutsoukos, "Efficient Simulation of Hybrid Systems: A Hybrid Bond Graph Approach," Simulation: Transactions of The Society for Modeling and Simulation International, vol. 87, no. 6, pp. 467-498, June 2011. [show abstract]
    Abstract: Accurate and efficient simulations facilitate cost-effective design and analysis of large, complex, embedded systems, whose behaviors are typically hybrid, i.e., continuous behaviors interspersed with discrete mode changes. This paper presents an approach for deriving component-based computational models of hybrid systems using hybrid bond graphs (HBGs), a multi-domain, energy-based modeling language that provides a compact framework for modeling hybrid physical systems. Our approach exploits the causality information inherent in HBGs to derive component-based computational models of hybrid systems as reconfigurable block diagrams. Typically, only small parts of the computational structure of a hybrid system change when mode changes occur. Our key idea is to identify the bonds and elements of HBGs whose causal assignments are invariant across system modes, and use this information to derive space-efficient reconfigurable block diagram models that may be reconfigured efficiently when mode changes occur. This reconfiguration is based on the incremental reassignment of causality implemented as the Hybrid Sequential Causal Assignment Procedure, which reassigns causality for the new mode based on the causal assignment of the previous mode. The reconfigurable block diagrams are general, and they can be transformed into simulation models for generating system behavior. Our modeling and simulation methodology, implemented as the Modeling and Transformation of HBGs for Simulation (MoTHS) tool suite, includes a component-based HBG modeling paradigm and a set of model translators for translating the HBG models into executable models. In this work, we use MoTHS to build a high-fidelity MATLAB Simulink model of an electrical power distribution system.
  • M. Daigle and K. Goebel, "Prognostics for Ground Support Systems: Case Study on Pneumatic Valves," Proceedings of AIAA Infotech@Aerospace 2011 Conference, St. Louis, MO, March 2011. [show abstract]
    Abstract: Prognostics technologies determine the health (or damage) state of a component or subsystem, and make end of life (EOL) and remaining useful life (RUL) predictions. Such information enables system operators to make informed maintenance decisions and streamline operational and mission-level activities. We develop a model-based prognostics methodology for pneumatic valves used in ground support equipment for cryogenic propellant loading operations. These valves are used to control the flow of propellant, so failures may have a significant impact on launch availability. Therefore, correctly predicting when valves will fail enables timely maintenance that avoids launch delays and aborts. The approach utilizes mathematical models describing the underlying physics of valve degradation, and, employing the particle filtering algorithm for joint state-parameter estimation, determines the health state of the valve and the rate of damage progression, from which EOL and RUL predictions are made. We develop a prototype user interface for valve prognostics, and demonstrate the prognostics approach using historical pneumatic valve data from the Space Shuttle refueling system.
  • M. Daigle and K. Goebel, "Multiple Damage Progression Paths in Model-based Prognostics," Proceedings of the 2011 IEEE Aerospace Conference, Big Sky, MT, March 2011. [show abstract]
    Abstract: Model-based prognostics approaches employ domain knowledge about a system, its components, and how they fail through the use of physics-based models. Component wear is driven by several different degradation phenomena, each resulting in their own damage progression path, overlapping to contribute to the overall degradation of the component. We develop a model-based prognostics methodology using particle filters, in which the problem of characterizing multiple damage progression paths is cast as a joint state-parameter estimation problem. The estimate is represented as a probability distribution, allowing the prediction of end of life and remaining useful life within a probabilistic framework that supports uncertainty management. We also develop a novel variance control mechanism that maintains an uncertainty bound around the hidden parameters to limit the amount of estimation uncertainty and, consequently, reduce prediction uncertainty. We construct a detailed physics-based model of a centrifugal pump, to which we apply our model-based prognostics algorithms. We illustrate the operation of the prognostic solution with a number of simulation-based experiments and demonstrate the performance of the chosen approach when multiple damage mechanisms are active.
  • M. Daigle, M. Foygel, and V. Smelyanskiy, "Model-based Diagnostics for Propellant Loading Systems," Proceedings of the 2011 IEEE Aerospace Conference, Big Sky, MT, March 2011. [show abstract]
    Abstract: The loading of spacecraft propellants is a complex, risky operation. Therefore, diagnostic solutions are necessary to quickly identify when a fault occurs, so that recovery actions can be taken or an abort procedure can be initiated. Model-based diagnosis solutions, established using an in-depth analysis and understanding of the underlying physical processes, offer the advanced capability to quickly detect and isolate faults, identify their severity, and predict their effects on system performance. We develop a physics-based model of a cryogenic propellant loading system, which describes the complex dynamics of liquid hydrogen filling from a storage tank to an external vehicle tank, as well as the influence of different faults on this process. The model takes into account the main physical processes such as highly non-equilibrium condensation and evaporation of the hydrogen vapor, pressurization, and also the dynamics of liquid hydrogen and vapor flows inside the system in the presence of helium gas. Since the model incorporates multiple faults in the system, it provides a suitable framework for model-based diagnostics and prognostics algorithms. Using this model, we analyze the effects of faults on the system, derive symbolic fault signatures for the purposes of fault isolation, and perform fault identification using a particle filter approach. We demonstrate the detection, isolation, and identification of a number of faults using simulation-based experiments.
2010
  • M. Daigle, X. Koutsoukos, and G. Biswas, "An Event-based Approach to Integrated Parametric and Discrete Fault Diagnosis in Hybrid Systems," Transactions of the Institute of Measurement and Control, Special Issue on Hybrid and Switched Systems, vol. 32, no. 5, pp. 487-510, October 2010. [show abstract]
    Abstract: Fault diagnosis is crucial for ensuring the safe operation of complex engineering systems. These systems often exhibit hybrid behaviors, therefore, model-based diagnosis methods have to be based on hybrid system models. Most previous work in hybrid systems diagnosis has focused either on parametric or discrete faults. In this paper, we develop an integrated approach for hybrid diagnosis of parametric and discrete faults by incorporating the effects of both types of faults into our event-based qualitative fault signature framework. The framework allows for systematic design of event-based diagnosers that facilitate diagnosability analysis. Experimental results from a case study performed on an electrical power distribution system demonstrate the effectiveness of the approach.
  • M. Daigle and I. Roychoudhury, "Qualitative Event-based Diagnosis: Case Study on the Second International Diagnostic Competition," Proceedings of the 21st International Workshop on Principles of Diagnosis, pp. 371-378, Portland, OR, October 2010. [show abstract]
    Abstract: We describe a diagnosis algorithm entered into the Second International Diagnostic Competition. We focus on the first diagnostic problem of the industrial track of the competition in which a diagnosis algorithm must detect, isolate, and identify faults in an electrical power distribution testbed and provide corresponding recovery recommendations. The diagnosis algorithm embodies a model-based approach, centered around qualitative event-based fault isolation. Faults produce deviations in measured values from model-predicted values. The sequence of these deviations is matched to those predicted by the model in order to isolate faults. We augment this approach with model-based fault identification, which determines fault parameters and helps to further isolate faults. We describe the diagnosis approach, provide diagnosis results from running the algorithm on provided example scenarios, and discuss the issues faced, and lessons learned, from implementing the approach.
  • M. Daigle, I. Roychoudhury, G. Biswas, and X. Koutsoukos, "An Event-based Approach to Distributed Diagnosis of Continuous Systems," Proceedings of the 21st International Workshop on Principles of Diagnosis, pp. 15-22, Portland, OR, October 2010. [show abstract]
    Abstract: Distributed fault diagnosis solutions are becoming necessary due to the complexity of modern engineering systems, and the advent of smart sensors and computing elements. This paper presents a novel event-based approach for distributed diagnosis of abrupt parametric faults in continuous systems, based on a qualitative abstraction of measurement deviations from the nominal behavior. We systematically derive dynamic fault signatures expressed as event-based fault models. We develop a distributed diagnoser design algorithm that uses these models for designing local event-based diagnosers based on global diagnosability analysis. The local diagnosers each generate globally correct diagnosis results locally, without a centralized coordinator, by communicating a minimal number of measurements between themselves. The proposed approach is applied to a multi-tank system, and results demonstrate a marked improvement in scalability compared to a centralized approach.
  • M. Daigle and K. Goebel, "Improving Computational Efficiency of Prediction in Model-based Prognostics Using the Unscented Transform," Proceedings of the Annual Conference of the Prognostics and Health Management Society 2010, Portland, OR, October 2010. [show abstract]
    Abstract: Model-based prognostics captures system knowledge in the form of physics-based models of components, and how they fail, in order to obtain accurate predictions of end of life (EOL). EOL is predicted based on the estimated current state distribution of a component and expected profiles of future usage. In general, this requires simulations of the component using the underlying models. In this paper, we develop a simulation-based prediction methodology that achieves computational efficiency by performing only the minimal number of simulations needed in order to accurately approximate the mean and variance of the complete EOL distribution. This is performed through the use of the unscented transform, which predicts the means and covariances of a distribution passed through a nonlinear transformation. In this case, the EOL simulation acts as that nonlinear transformation. In this paper, we review the unscented transform, and describe how this concept is applied to efficient EOL prediction. As a case study, we develop a physics-based model of a solenoid valve, and perform simulation experiments to demonstrate improved computational efficiency without sacrificing prediction accuracy.
  • M. Daigle, I. Roychoudhury, G. Biswas, X. Koutsoukos, A. Patterson-Hine, and S. Poll, "A Comprehensive Diagnosis Methodology for Complex Hybrid Systems: A Case Study on Spacecraft Power Distribution Systems," IEEE Transactions of Systems, Man, and Cybernetics, Part A, Special Section on Model-based Diagnosis: Facing Challenges in Real-world Applications, vol. 4, no. 5, pp. 917-931, September 2010. [show abstract]
    Abstract: The application of model-based diagnosis schemes to real systems introduces many significant challenges, such as building accurate system models for heterogeneous systems with complex behaviors, dealing with noisy measurements and disturbances, and producing valuable results in a timely manner with limited information and computational resources. The Advanced Diagnostics and Prognostics Testbed (ADAPT), deployed at NASA Ames Research Center, is a representative spacecraft electrical power distribution system that embodies a number of these challenges. ADAPT contains a large number of interconnected components, and a set of circuit breakers and relays that enable a number of distinct power distribution configurations. The system includes electrical dc and ac loads, mechanical subsystems, such as motors, and fluid systems, such as pumps. The system components are susceptible to different types of faults, i.e., unexpected changes in parameter values, discrete faults in switching elements, and sensor faults. This paper presents Hybrid TRANSCEND, a comprehensive model-based diagnosis scheme to address these challenges. The scheme uses the hybrid bond graph modeling language to systematically develop computational models and algorithms for hybrid state estimation, robust fault detection, and efficient fault isolation. The computational methods are implemented as a suite of software tools that enable diagnostic analysis and testing through simulation, diagnosability studies, and deployment on the experimental testbed. Simulation and experimental results demonstrate the effectiveness of the methodology.
  • V. Osipov, C. Muratov, M. Daigle, M. Foygel, V. Smelyanskiy, and A. Patterson-Hine, A Dynamical Physics Model of Nominal and Faulty Operational Modes of Propellant Loading (Liquid Hydrogen): From Space Shuttle to Future Missions," NASA/TM-2010-216394, NASA Ames Research Center, July 2010. [show abstract]
    Abstract:
  • M. S. Feather, K. Goebel, and M. Daigle, "Tackling Verification and Validation for Prognostics," Proceedings of the SpaceOps 2010 Conference, Hunstville, AL, April 2010. [show abstract]
    Abstract: Verification and validation (V&V) has been identified as a critical phase in fielding systems with Integrated Systems Health Management (ISHM) solutions to ensure that the results produced are robust, reliable, and can confidently inform about vehicle and system health status and to support operational and maintenance decisions. Prognostics is a key constituent within ISHM. It faces unique challenges for V&V since it informs about the future behavior of a component or subsystem. In this paper, we present a detailed review of identified barriers and solutions to prognostics V&V, and a novel methodological way for the organization and application of this knowledge. We discuss these issues within the context of a prognostics application for the ground support equipment of space vehicle propellant loading, and identify the significant barriers and adopted solution for this application.
  • A. Moustafa, S. Mahadevan, M. Daigle, and G. Biswas, Structural and Sensor Damage Identification using the Bond Graph Approach," Structural Control and Health Monitoring, vol. 17, no. 2, pp. 178-197, March 2010. [show abstract]
    Abstract: This paper develops a new and efficient hybrid qualitative-quantitative system identification methodology for structures using the bond graph approach. Bond graphs provide a modeling framework that includes parametric models of both the physical system and the sensors. Structural damage is modeled as reductions in the parameter values of the structural components. Sensor faults are modeled as biases or drifts from true responses. The damage detection uses a statistical method to identify significant deviations of measurements from nominal healthy behavior of the structure. Damage isolation is carried out by comparing the predicted signatures of various damage scenarios with the observed behavior of the structure. The damage signatures are derived off-line before sensor data collection. Quantitative identification of the damage amount uses the least-squares method, analyzing only the sub-structure containing the damaged component. Numerical illustrations of damage identification of frame structures driven by time-varying loads are provided, highlighting the advantages with respect to sensor fault identification and computational efficiency.
  • M. Daigle and K. Goebel, "Model-based Prognostics under Limited Sensing," 2010 IEEE Aerospace Conference, Big Sky, MT, March 2010. [show abstract]
    Abstract: Prognostics is crucial to providing reliable condition-based maintenance decisions. To obtain accurate predictions of component life, a variety of sensors are often needed. However, it is typically difficult to add enough sensors for reliable prognosis, due to system constraints such as cost and weight. Model-based prognostics helps to offset this problem by exploiting domain knowledge about the system, its components, and how they fail by casting the underlying physical phenomena in a physics-based model that is derived from first principles. We develop a model-based prognostics methodology using particle filters, and investigate the benefits of a model-based approach when sensor sets are diminished. We apply our approach to a detailed physics-based model of a pneumatic valve, and perform comprehensive simulation experiments to demonstrate the robustness of model-based approaches under limited sensing scenarios using prognostics performance metrics.
2009
  • M. Daigle and K. Goebel, "Model-based Prognostics with Fixed-lag Particle Filters," Proceedings of the Annual Conference of the Prognostics and Health Management Society 2009, San Diego, CA, September 2009. [show abstract]
    Abstract: Model-based prognostics exploits domain knowledge of the system, its components, and how they fail by casting the underlying physical phenomena in a physics-based model that is derived from first principles. In most applications, uncertainties from a number of sources cause the predictions to be inaccurate and imprecise even with accurate models. Therefore, algorithms are employed that help in managing these uncertainties. Particle filters have become a popular choice to solve this problem due to their wide applicability and ease of implementation. We present a general model-based prognostics methodology using particle filters. In order to provide more accurate and precise estimates, and, therefore, more accurate and precise predictions, we investigate the use of fixed-lag filters. We develop a detailed physics-based model of a pneumatic valve, and perform comprehensive simulation experiments to illustrate our prognostics approach. The experiments demonstrate the advantages that fixed-lag filters may provide in the context of prognostics, as measured by prognostics performance metrics.
  • M. Daigle, X. Koutsoukos, and G. Biswas, "Improving Diagnosability of Hybrid Systems through Active Diagnosis," Proceedings of the 7th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes, pp. 217-222, Barcelona, Spain, July 2009. (invited paper) [show abstract]
    Abstract: Fault diagnosis is key to ensuring system safety through fault-adaptive control. This task is diffcult in hybrid systems with combined continuous and discrete behaviors because mode changes make diagnosability hard to achieve. Including additional sensors can improve diagnosability, but that is not always feasible. An alternative strategy is active diagnosis, where we improve the diagnosis result by executing or blocking controllable events. We present a qualitative, event-based approach to active diagnosis of hybrid systems, where we automatically synthesize event-based diagnosers for hybrid systems that can determine if the system is diagnosable through passive or active diagnosis. We apply our active diagnosis scheme to a real-world electrical power distribution system.
  • I. Roychoudhury, M. Daigle, G. Biswas, X. Koutsoukos, A. Patterson-Hine, and S. Poll, "Comprehensive Diagnosis of Complex Electrical Power Distribution Systems," Proceedings of the 7th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes, pp. 722-727, Barcelona, Spain, July 2009. (invited paper, session acceptance rate 33%) [show abstract]
    Abstract: Electrical power distribution systems are composed of heterogeneous components, which include continuous power sources, discrete relays, passive and active loads, and fast-switching power conversion subsystems. This heterogeneity introduces significant challenges for model-based diagnosis, such as building accurate models, and generating fast and accurate diagnoses while ensuring robustness to measurement noise and modeling errors. In this paper, we present a comprehensive methodology for the diagnosis of parametric and discrete faults in electrical power distribution systems that include dc and ac components. We use a hybrid bond graph modeling language to systematically develop computational models and algorithms for hybrid state estimation, robust fault detection, and efficient fault isolation. Simulation and experimental results on a real-world electrical power distribution system demonstrate the effectiveness of our methodology.
  • M. Daigle, X. Koutsoukos, and G. Biswas, "A Qualitative Event-based Approach to Continuous Systems Diagnosis," IEEE Transactions on Control Systems Technology, vol. 17, no. 4, pp. 780-793, July 2009. [show abstract]
    Abstract: Fault diagnosis is crucial for ensuring the safe operation of complex engineering systems. Although discrete-event diagnosis methods are used extensively, they do not easily address parametric fault isolation in systems with complex continuous dynamics. This paper presents a novel event-based approach for diagnosis of abrupt parametric faults in continuous systems, based on a qualitative abstraction of measurement deviations from the nominal behavior. From a continuous model of the system, we systematically derive dynamic fault signatures expressed as event-based fault models, which are used, in turn, for designing an event-based diagnoser of the system and determining system diagnosability. The proposed approach is applied to a subset of the Advanced Diagnostics and Prognostics Testbed, which is representative of a spacecraft's electrical power system. We present experimental results from the actual testbed, as well as detailed simulation experiments that examine the performance of our diagnosis algorithms under different fault magnitudes and noise levels.
  • C. Goodrich, S. Narasimhan, M. Daigle, W. Hatfield, R. Johnson, and B. Brown, "Applying Model-based Diagnosis to a Rapid Propellant Loading System," Proceedings of the 20th International Workshop on Principles of Diagnosis, pp. 147-154, Stockholm, Sweden, June 2009. [show abstract]
    Abstract: The overall objective of the US Air Force Research Laboratory (AFRL) Rapid Propellant Loading (RPL) Program is to develop a launch vehicle, payload and ground support equipment that can support a rapid propellant load and launch within one hour. NASA Kennedy Space Center (KSC) has been funded by AFRL to develop hardware and software to demonstrate this capability. The key features of the software would be the ability to recognize and adapt to failures in the physical hardware components, advise operators of equipment faults and workarounds, and put the system in a safe configuration if unable to fly. In December 2008 NASA KSC and NASA Ames Research Center (ARC) demonstrated model-based simulation and diagnosis capabilities for a scaled-down configuration of the RPL hardware. In this paper we present a description of the model-based technologies that were included as part of this demonstration and the results that were achieved. In continuation of this work we are currently testing the technologies on a simulation of the complete RPL system. Later in the year, when the RPL hardware is ready, we will be integrating these technologies with the real-time operation of the system to provide live state estimates. In future years we will be developing the capability to recover from faulty conditions via redundancy and reconfiguration.
2008
  • M. Daigle, I. Roychoudhury, G. Biswas, X. Koutsoukos, A. Patterson-Hine, and S. Poll, "A Comprehensive Diagnosis Methodology for Complex Hybrid Systems: A Case Study on Spacecraft Power Distribution Systems," Technical Report ISIS-08-908, Institute for Software Integrated Systems, Vanderbilt University, December 2008. [show abstract]
    Abstract: The application of model-based diagnosis schemes to real systems introduces many significant challenges, such as building accurate system models for heterogeneous systems with complex behaviors, dealing with noisy measurements and disturbances during system operation, and producing valuable results in a timely manner with limited information and computational resources. The Advanced Diagnostics and Prognostics Testbed (ADAPT), deployed at NASA Ames Research Center, is a representative spacecraft electrical power distribution system that embodies a number of these challenges for developing realistic diagnosis and prognosis algorithms. ADAPT contains a large number of interconnected components, along with a number of circuit breakers and relays that enable a number of different power distribution configurations. The system includes electrical dc and ac loads, mechanical subsystems, such as motors, and fluid systems, such as pumps. The system components are susceptible to different types of faults that include unexpected changes in parameter values, discrete faults in switching elements, and sensor faults. This paper presents Hybrid TRANSCEND, a comprehensive model-based diagnosis scheme to address these challenges. The scheme uses the hybrid bond graph modeling language to systematically develop computational models and algorithms for hybrid state estimation, robust fault detection, and efficient fault isolation. The computational methods are implemented as a suite of software tools that enables analysis and testing through simulation, diagnosability studies, and deployment on the experimental testbed. Simulation and experimental results demonstrate the effectiveness of this methodology in efficient diagnosis of heterogeneous components for an embedded system.
  • I. Roychoudhury, M. Daigle, G. Biswas, and X. Koutsoukos, "An Efficient Method for Simulating Complex Systems with Switching Behaviors Using Hybrid Bond Graphs," Simulation News Europe, vol. 18, no. 3-4, pp. 5-13, December 2008. [show abstract]
    Abstract: Accurate and efficient modeling and simulation approaches are essential for design, analysis, diagnosis, and prognosis of complex embedded systems. This paper presents an efficient simulation scheme for systems with mixed continuous and discrete behaviors. We model hybrid systems using hybrid bond graphs (HBGs), a multi-domain physics-based modeling language that incorporates local switching functions that enable the reconfiguration of energy flow paths. We exploit the inherent causal structure in HBGs to derive hybrid simulation models as reconfigurable block diagram (BD) structures. Considerable computational savings are achieved during simulation by identifying fixed causal assignments when the simulation model is derived. Fixed causal assignments reduce the number of possible computational structures across all mode changes, and this leads to an overall reduction in the complexity of the BD simulation models and in their reconfiguration procedures. This approach has been implemented as a software tool called the MOdeling and Transformation of HBGs for Simulation (MOTHS) tool suite. Simulation models of an electrical power distribution system that includes a fast switching inverter system are derived using the MOTHS tool suite, and experimental studies on this system demonstrate the effectiveness of our approach.
  • M. Daigle, X. Koutsoukos, and G. Biswas, "An Event-based Approach to Hybrid Systems Diagnosability," Proceedings of the 19th International Workshop on Principles of Diagnosis, pp. 47-54, September 2008. [show abstract]
    Abstract: Diagnosability is an important issue in the design of diagnostic systems, because it helps identify whether sufficient information is available to distinguish all the faults. Diagnosability of hybrid systems, however, is challenging, because mode transitions may occur during fault isolation. We present an event-based framework for hybrid systems diagnosis based on a qualitative abstraction of measurement deviations from nominal behavior. We derive event-based fault models that describe the possible measurement deviations sequences due to faults, which, coupled with the mode transition structure of the system, are used to automatically synthesize an event-based diagnoser for hybrid systems. We introduce notions of diagnosability for hybrid systems and show how the event-based diagnoser can be used to verify the diagnosability of the system. We apply our diagnosability analysis scheme to a real-world electrical power distribution system.
  • I. Roychoudhury, M. Daigle, G. Biswas, and X. Koutsoukos, "Efficient Simulation of Hybrid Systems: An Application to Electrical Power Distribution Systems," Proceedings of the 22nd European Conference on Modeling and Simulation, pp. 471-477, Nicosia, Cyprus, June 2008. [show abstract]
    Abstract: This paper presents an efficient simulation scheme for hybrid systems modeled as hybrid bond graphs (HBGs). Considerable computational savings are achieved when mode changes occur during simulation by identifying persistent causal assignments to bonds, and, consequently, fixed causal structures at HBG junctions when the simulation model is derived. Persistent causal assignments also reduce the possible computational structures across all mode changes, and this leads to an overall reduction in the complexity of the simulation models. We demonstrate the benefits of our approach for an electrical power distribution system that includes a fast switching inverter system.
  • M. Daigle, "A Qualitative Event-based Approach to Fault Diagnosis of Hybrid Systems," Vanderbilt University, May 2008. [show abstract]
    Abstract: Fault diagnosis is crucial for ensuring the safe operation of complex engineering systems. Many present-day systems combine physical and computational processes, and are best modeled as hybrid systems, where the dynamic behavior combines continuous evolution interspersed with discrete configuration changes. Due to the complexity of such modern engineering systems, formal methods are required for reliable and correct design, analysis, and implementation of hybrid system diagnosers. This Dissertation presents a systematic, model-based approach to event-based diagnosis of hybrid systems based on qualitative abstractions of deviations from nominal behavior. The primary contributions of this work center on (i) incorporating relative measurement orderings into fault isolation for continuous and hybrid systems, which describe predicted temporal orderings of measurement deviations, (ii) providing algorithms for event-based diagnosis of single and multiple faults, (iii) developing an integrated framework for diagnosis of parametric, sensor, and discrete, i.e., switching faults in hybrid systems, and (iv) developing and implementing an efficient event-based diagnosis framework for continuous and hybrid systems that enables automatic design of event-based diagnosers and establishes notions of diagnosability for continuous and hybrid systems. The effectiveness of the approach is demonstrated on two practical systems. First, the single fault diagnosis method for continuous systems is applied in a distributed fashion to formations of mobile robots. The results include a formal diagnosability analysis, scalability results, and experiments performed on a formation of robots. Second, the approach developed for hybrid systems diagnosis is applied to the Advanced Diagnostics and Prognostics Testbed, which is a complex electrical distribution system for spacecraft and aircraft applications. The results focus on a subset of the testbed, and include a diagnosability analysis, experiments from the actual testbed, and detailed simulation experiments that examine the performance of the diagnosis algorithms for different fault magnitudes and noise levels.
  • M. Daigle, X. Koutsoukos, and G. Biswas, "An Integrated Approach to Parametric and Discrete Fault Diagnosis in Hybrid Systems," Hybrid Systems: Computation and Control (HSCC 2008), Lecture Notes in Computer Science, vol. 4981, pp. 614-617, St. Louis, MO, April 2008. (short paper) [show abstract]
    Abstract:
  • M. Daigle, X. Koutsoukos, and G. Biswas, "An Integrated Approach to Parametric and Discrete Fault Diagnosis in Hybrid Systems," Technical Report ISIS-07-815, Institute for Software Integrated Systems, Vanderbilt University, January 2008. [show abstract]
    Abstract: Fault diagnosis is crucial for ensuring the safe operation of complex engineering systems. These systems are typically hybrid in nature, therefore, model-based diagnosis requires hybrid system models. Previous work in hybrid systems diagnosis, however, has focused either on parametric or discrete faults. We present an integrated approach for diagnosis of both parametric and discrete faults in hybrid systems that encompasses a compact hybrid systems modeling approach and an efficient qualitative fault isolation scheme. Experimental results from a case study performed on a complex electrical power system demonstrate the effectiveness of the approach.
2007
  • M. Daigle, X. Koutsoukos, and G. Biswas, "Fault Diagnosis of Continuous Systems Using Discrete-Event Methods," Proceedings of the 46th IEEE Conference on Decision and Control, pp. 2626-2632, New Orleans, LA, Dec 2007. [show abstract]
    Abstract: Fault diagnosis is crucial for ensuring the safe operation of complex engineering systems. Although discrete-event diagnosis methods are used extensively, they do not easily apply to parametric fault isolation in systems with complex continuous dynamics. This paper presents a novel discrete-event system diagnosis approach for abrupt parametric faults in continuous systems that is based on a qualitative abstraction of measurement deviations from the nominal behavior. Our approach systematically generates a diagnosis model from bond graphs that is used to analyze system diagnosability and derive the discrete-event diagnoser. The proposed approach is applied to an electrical power system diagnostic testbed.
  • M. Daigle, X. Koutsoukos, and G. Biswas, "A Qualitative Approach to Multiple Fault Isolation in Continuous Systems," Proceedings of the Twenty-Second AAAI National Conference 2007, pp. 293-298, Vancouver, British Columbia, Canada, July 2007. (acceptance rate 27%) [show abstract]
    Abstract: The multiple fault diagnosis problem is important, since the single fault assumption can lead to incorrect or failed diagnoses when multiple faults occur. It is challenging for continuous systems, because faults can mask or compensate each other's effects, and the solution space grows exponentially with the number of possible faults. We present a qualitative approach to multiple fault isolation in dynamic systems based on analysis of fault transient behavior. Our approach uses the observed measurement deviations and their temporal orderings to generate multiple fault hypotheses. The approach has polynomial space requirements and prunes diagnoses, resulting in an efficient online fault isolation scheme.
  • M. Daigle, X. Koutsoukos, and G. Biswas, "On Discrete Event Diagnosis Methods for Continuous Systems," Proceedings of the 15th IEEE Mediterranean Conference on Control and Automation, June 2007. [show abstract]
    Abstract: Fault detection and isolation is a key component of any safety-critical system. Although diagnosis methods based on discrete event systems have been recognized as a promising framework, they cannot be easily applied to systems with complex continuous dynamics. This paper presents a novel approach for discrete event system diagnosis of continuous systems based on a qualitative abstraction of the measurement deviations from the nominal behavior. We systematically derive a diagnosis model, provide diagnosability analysis, and design a diagnoser. Our results show that the proposed approach is easily applicable and can be used for online diagnosis of abrupt faults in continuous systems.
  • M. Daigle, X. Koutsoukos, and G. Biswas, "A Discrete Event Approach to Diagnosis of Continuous Systems," Proceedings of the 18th International Workshop on Principles of Diagnosis, pp. 259-266, Nashville, TN, May 2007. [show abstract]
    Abstract: Fault detection and isolation is a key component of any safety-critical system. Although diagnosis methods based on discrete event systems have been recognized as a promising framework, they cannot be easily applied to systems with complex continuous dynamics. This paper presents a novel approach for discrete event system diagnosis of continuous systems based on a qualitative abstraction of the measurement deviations from the nominal behavior. We systematically derive a diagnosis model, provide diagnosability analysis, and design a diagnoser. Our results show that the proposed approach is easily applicable and can be used for online diagnosis of abrupt faults in continuous systems.
  • S. Poll, A. Patterson-Hine, J. Camisa, D. Garcia, D. Hall, C. Lee, O. Mengshoel, C. Neukom, D. Nishikawa, J. Ossenfort, A. Sweet, S. Yentus, I. Roychoudhury, M. Daigle, G. Biswas, and X. Koutsoukos, "Advanced Diagnostics and Prognostics Testbed," Proceedings of the 18th International Workshop on Principles of Diagnosis, pp. 178-185, Nashville, TN, May 2007. [show abstract]
    Abstract: Researchers in the diagnosis community have developed a number of promising techniques for system health management. However, realistic empirical evaluation and comparison of these approaches is often hampered by a lack of standard data sets and suitable testbeds. In this paper we describe the Advanced Diagnostics and Prognostics Testbed (ADAPT) at NASA Ames Research Center. The purpose of the testbed is to measure, evaluate, and mature diagnostic and prognostic health management technologies. This paper describes the testbed's hardware, software architecture, and concept of operations. A simulation testbed that accompanies ADAPT, and some of the diagnostic and decision support approaches being investigated are also discussed.
  • A. Moustafa, M. Daigle, I. Roychoudhury, C. Shantz, G. Biswas, S. Mahadevan, and X. Koutsoukos, "Fault Diagnosis of Civil Engineering Structures using the Bond Graph Approach," Proceedings of the 18th International Workshop on Principles of Diagnosis, pp. 146-153, Nashville, TN, May 2007. [show abstract]
    Abstract: This paper develops a fault diagnosis methodology for civil engineering structures based on the bond graph approach. The bond graph theory provides a modeling framework that includes parametric models of the physical system and the sensors. Structural faults are modeled as abrupt or gradual damage in structural components. Sensor faults are modeled as biases or drifts from true measurements. Fault detection uses a statistical method to identify significant deviations of measurements from nominal behavior of the structure. Fault isolation is carried out by comparing predicted effects of hypothesized faults with observed behavior of the structure. Numerical illustrations of fault diagnosis of a frame structure driven by time-varying loads are provided.
  • S. Poll, A. Patterson-Hine, J. Camisa, D. Nishikawa, L. Spirkovska, D. Garcia, D. Hall, C. Neukom, A. Sweet, S. Yentus, C. Lee, J. Ossenfort, I. Roychoudhury, M. Daigle, G. Biswas, X. Koutsoukos, and R. Lutz, "Evaluation, Selection, and Application of Model-Based Diagnosis Tools and Approaches," AIAA Infotech@Aerospace 2007 Conference and Exhibit, May 2007. [show abstract]
    Abstract: Model-based approaches have proven fruitful in the design and implementation of intelligent systems that provide automated diagnostic functions. A wide variety of models are used in these approaches to represent the particular domain knowledge, including analytic state-based models, input-output transfer function models, fault propagation models, and qualitative and quantitative physics-based models. Diagnostic applications are built around three main steps: observation, comparison, and diagnosis. If the modeling begins in the early stages of system development, engineering models such as fault propagation models can be used for testability analysis to aid definition and evaluation of instrumentation suites for observation of system behavior. Analytical models can be used in the design of monitoring algorithms that process observations to provide information for the second step in the process, comparison of expected behavior of the system to actual measured behavior. In the final diagnostic step, reasoning about the results of the comparison can be performed in a variety of ways, such as dependency matrices, graph propagation, constraint propagation, and state estimation. Realistic empirical evaluation and comparison of these approaches is often hampered by a lack of standard data sets and suitable testbeds. In this paper we describe the Advanced Diagnostics and Prognostics Testbed (ADAPT) at NASA Ames Research Center. The purpose of the testbed is to measure, evaluate, and mature diagnostic and prognostic health management technologies. This paper describes the testbed's hardware, software architecture, and concept of operations. A simulation testbed that accompanies ADAPT, and some of the diagnostic and decision support approaches being investigated are also discussed.
  • M. Daigle, X. Koutsoukos, and G. Biswas, "Distributed Diagnosis in Formations of Mobile Robots," IEEE Transactions on Robotics, vol. 23, no. 2, pp. 353-369, April 2007. [show abstract]
    Abstract: Multi-robot systems are being increasingly used for a variety of tasks in manufacturing, surveillance, and space exploration. These systems can degrade or develop faults during operation, and, therefore, require online diagnosis algorithms to ensure safe operation. Centralized approaches to online diagnosis of robot formations do not scale well for two primary reasons: (i) the computational complexity of the algorithm grows significantly with the number of robots, and (ii) the individual robots must communicate a large number of measurements to a central diagnoser. To overcome these problems, we present a distributed, model-based, qualitative fault diagnosis approach for formations of mobile robots. The approach is based on a bond graph modeling framework that can deal with multiple sensor types and isolate process, sensor, and actuator faults. The diagnosis scheme employs relative measurement orderings to discriminate among faults by exploiting the temporal order of measurement deviations. This increases the discriminatory power of the measurement set and produces a more efficient fault isolation algorithm. We describe a distributed diagnoser design algorithm applied to robot formations. Experimental results demonstrate the improvement in both the discriminatory power of the measurements produced by the relative measurement orderings, and the computational efficiency achieved by the distributed diagnosis approach.
  • M. Daigle, I. Roychoudhury, G. Biswas, and X. Koutsoukos, "Efficient Simulation of Component-Based Hybrid Models Represented as Hybrid Bond Graphs," Hybrid Systems: Computation and Control (HSCC 2007), Lecture Notes in Computer Science, vol. 4416, pp. 680-683, Pisa, Italy, April 2007. (short paper) [show abstract]
    Abstract:
  • I. Roychoudhury, M. Daigle, X. Koutsoukos, G. Biswas, and P. J. Mosterman, "A Method for Efficient Simulation of Hybrid Bond Graphs," Proceedings of the International Conference on Bond Graph Modeling and Simulation (ICBGM 2007), pp. 177-184, San Diego, CA, January 2007. [show abstract]
    Abstract: The hybrid bond graph (HBG) paradigm is a uniform, multi-domain physics-based modeling language. It incorporates controlled and autonomous mode changes as idealized switching functions that enable the reconfiguration of energy flow paths to model hybrid physical systems. Building accurate and computationally efficient simulation mechanisms from HBG models is a challenging task, especially when there is no a priori knowledge of the subset of system modes that will be active during the simulation. In this work, we present an approach that exploits the inherent causal structure in HBG models to derive efficient hybrid simulation models as reconfigurable block diagram structures. We present a MATLAB Simulink implementation of our approach and demonstrate its effectiveness using an electrical circuit example.
2006
  • M. Daigle, I. Roychoudhury, G. Biswas, and X. Koutsoukos, "Efficient Simulation of Component-Based Hybrid Models Represented as Hybrid Bond Graphs," Technical Report ISIS-06-712, Institute for Software Integrated Systems, Vanderbilt University, December 2006. [show abstract]
    Abstract: The complexity of modern embedded systems often requires the use of simulations for systematic design, analysis, and verification tasks. The nonlinear and hybrid nature of these systems make the building of accurate and computationally efficient simulation models very challenging. In this work, we adopt the Hybrid Bond Graph (HBG) paradigm, a uniform, multi-domain physics-based modeling language with local switching functions that enable the reconfiguration of energy flow paths to model hybrid systems. The inherent causal structure in HBG models is exploited to derive efficient hybrid simulation models as reconfigurable block diagram structures. We demonstrate our approach by modeling and analyzing the behavior of an electrical power system.
  • M. Daigle, X. Koutsoukos, and G. Biswas, "Multiple Fault Diagnosis in Complex Physical Systems," Proceedings of the 17th International Workshop on Principles of Diagnosis, pp. 69-76, June 2006. [show abstract]
    Abstract: Multiple fault diagnosis is a challenging problem because the number of candidates grows exponentially in the number of faults. In addition, multiple faults in dynamic systems may be hard to detect, because they can mask or compensate each other's effects. The multiple fault problem is important, since the single fault assumption can lead to incorrect or failed diagnoses when multiple faults occur. We present an approach to simultaneous and cascaded multiple fault diagnosis in dynamical systems. Our approach is based on the TRANSCEND fault isolation scheme, where fault effects are represented as qualitative fault signatures. A notion of multiple fault diagnosability is introduced with respect to most likely minimal candidates. The online fault isolation algorithm explores the candidate space in increasing candidate size to generate minimal candidates. A mobile robot example demonstrates the approach.
  • M. Daigle, X. Koutsoukos, and G. Biswas, "Distributed Diagnosis of Coupled Mobile Robots," Proceedings of the 2006 IEEE International Conference on Robotics and Automation, pp. 3787-3794, Orlando, FL, May 2006. (acceptance rate 39%) [show abstract]
    Abstract: Fault diagnosis of coupled mobile robots requires a large number of measurements to be communicated either between the robots or from the robots to a central diagnoser. As computational complexity increases with the number of measurements, centralized algorithms become inefficient. This paper presents a distributed approach for qualitative fault diagnosis of coupled mobile robots. The approach is based on a bond graph modeling framework which incorporates local and distributed control algorithms, multiple sensor types, and both actuator and sensor faults. Relative measurement orderings are introduced to discriminate faults by exploiting the temporal order of the measurement deviations. This increases the discriminatory power of a set of measurements and results in a more efficient qualitative diagnosis algorithm. Distributed diagnosers are designed and applied to coupled mobile robots. Experimental results for a system consisting of two robots pushing a box demonstrate the improvement in both discriminatory power of the measurements and efficiency of the distributed diagnosis approach.
2005
  • S. Bringsjord, S. Khemlani, K. Arkoudas, C. McEvoy, M. Destefano, and M. Daigle, Advanced Synthetic Characters, Evil, and E," Game-On 2005, 6th International Conference on Intelligent Games and Simulation, pp. 31-39, Leicester, United Kingdom, November 2005. (won best paper award) [show abstract]
    Abstract: We describe our approach to building advanced synthetic characters, within the paradigm of logic-based AI. Such characters don't merely evoke beliefs that they have various mental properties; rather, they must actually have such properties. You might (e.g.) believe a standard synthetic character to be evil, but you would of course be wrong. An advanced synthetic character, however, can literally be evil, because it has the requisite desires, beliefs, and cognitive powers. Our approach is based on our RASCALS architecture, which uses simple logical systems (first-order ones) for low-level (perception & action) and mid-level cognition, and advanced logical systems (e.g., epistemic and deontic logics) for more abstract cognition. To focus our approach herein, we provide a glimpse of our attempt to bring to life one particular advanced synthetic character from the ''dark side'' - the evil character known simply as E. Building E entails that, among other things, we formulate an underlying logico-mathematical definition of evil, and that we manage to engineer both an appropriate presentation of E, and communication between E and humans. For presentation, which we only encapsulate here, we use several techniques, including muscle simulation in graphics hardware and approximation of subsurface scattering. For communication, we use our own new ''proof-based'' approach to Natural Language Generation (NLG). We provide an account of this approach.
  • M. Daigle, X. Koutsoukos, and G. Biswas, "Relative Measurement Orderings in Diagnosis of Distributed Physical Systems," Proceedings of the 43rd Annual Allerton Conference on Communication, Control, and Computing, pp. 1707-1716, Allerton, IL, September 2005. (invited paper) [show abstract]
    Abstract: Fault diagnosis in large-scale, distributed physical systems often requires the use of a large number of measurements to achieve complete diagnosability. The computational complexity of the diagnosis algorithm increases with the number of measurements, making centralized approaches infeasible for online analysis. This paper presents an extension to the Transcend framework for qualitative fault diagnosis in complex physical systems. The Transcend framework is based solely on qualitative time-derivative effects. Our approach combines relative measurement orderings with the traditional fault signature approach to increase the discriminatory power of a set of measurements. The measurement orderings are based on a qualitative analysis of the temporal propagation of fault effects derived from the temporal causal graph of the system. These orderings allow for diagnosis with fewer measurements. More importantly, in large-scale systems, the orderings can be used to reduce the number of measurements used by local diagnosers, leading to more efficient algorithms. The application of the approach to large-scale, distributed systems is illustrated using a multi-tank system.