Zonotopic fault estimation filter design for discrete-time descriptor systems

This paper considers actuator-fault estimation for discrete-time descriptor systems with unknown but bounded system disturbance and measurement noise. A zonotopic fault estimation filter is designed based on the analysis of fault detectability indexes. To ensure estimation accuracy, the filter gain in the zonotopic fault estimation filter is optimized through the zonotope minimization. The designed zonotopic filter not only can estimate fault magnitudes, but it also provides fault estimation results in an interval, i.e. the upper and lower bounds of fault magnitudes. Moreover, the proposed fault estimation filter has a non-singular structure and hence is easy to implement. Finally, simulation results are provided to illustrate the effectiveness of the proposed method.Postprint (published version

Correct-By-Construction Fault-Tolerant Control

Correct-by-construction control synthesis methods refer to a collection of model-based techniques to algorithmically generate controllers/strategies that make the systems satisfy some formal specifications. Such techniques attract much attention as they provide formal guarantees on the correctness of cyber-physical systems, where corner cases may arise due to the interaction among different modules. The controllers synthesized through such methods, however, may still malfunction due to faults, such as physical component failures and unexpected operating conditions, which lead to a sudden change of the system model. In these cases, we want to guarantee that the performance of the faulty system degrades gracefully, and hence achieve fault tolerance. This thesis is about 1) incorporating fault detection and detectability analysis algorithms in correct-by-construction control synthesis, 2) formalizing the graceful degradation specification for fault tolerant systems with temporal logic, and 3) developing algorithms to synthesize correct-by-construction controllers that achieve such graceful degradation, with possible delay in the fault detection. In particular, two sets of approaches from the temporal logic planning domain, i.e., abstraction-based synthesis and optimization-based path planning, are considered. First, for abstraction-based approaches, we propose a recursive algorithm to reduce the fault tolerant controller synthesis problem into multiple small synthesis problems with simpler specifications. Such recursive reduction leverages the structure of the fault propagation and hence avoids the high complexity of solving the problem monolithically as one general temporal logic game. Furthermore, by exploring the structural properties in the specifications, we show that, even when the fault is detected with delay, the problem can be solved by a similar recursive algorithm without constructing the belief space. Secondly, optimization-based path planning is considered. The proposed approach leverages the recently developed temporal logic encodings and state-of-art mixed integer programming (MIP) solvers. The novelty of this work is to enhance the open-loop strategy obtained through solving the MIP so that it can react contingently to faults and disturbance. Finally, the control synthesis techniques developed for discrete state systems is shown to be applicable to continuous states systems. This is demonstrated by fuel cell thermal management application. Particularly, to apply the abstraction-based synthesis methods to complex systems such as the fuel cell thermal management system, structural properties (e.g., mixed monotonicity) of the system are explored and leveraged to ease abstraction computation, and techniques are developed to improve the scalability of synthesis process whenever the system has a large number of control actions.PHDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/155031/1/yliren_1.pd

FAST : a fault detection and identification software tool

The aim of this work is to improve the reliability and safety of complex critical control systems by contributing to the systematic application of fault diagnosis. In order to ease the utilization of fault detection and isolation (FDI) tools in the industry, a systematic approach is required to allow the process engineers to analyze a system from this perspective. In this way, it should be possible to analyze this system to find if it provides the required fault diagnosis and redundancy according to the process criticality. In addition, it should be possible to evaluate what-if scenarios by slightly modifying the process (f.i. adding sensors or changing their placement) and evaluating the impact in terms of the fault diagnosis and redundancy possibilities. Hence, this work proposes an approach to analyze a process from the FDI perspective and for this purpose provides the tool FAST which covers from the analysis and design phase until the final FDI supervisor implementation in a real process. To synthesize the process information, a very simple format has been defined based on XML. This format provides the needed information to systematically perform the Structural Analysis of that process. Any process can be analyzed, the only restriction is that the models of the process components need to be available in the FAST tool. The processes are described in FAST in terms of process variables, components and relations and the tool performs the structural analysis of the process obtaining: (i) the structural matrix, (ii) the perfect matching, (iii) the analytical redundancy relations (if any) and (iv) the fault signature matrix. To aid in the analysis process, FAST can operate stand alone in simulation mode allowing the process engineer to evaluate the faults, its detectability and implement changes in the process components and topology to improve the diagnosis and redundancy capabilities. On the other hand, FAST can operate on-line connected to the process plant through an OPC interface. The OPC interface enables the possibility to connect to almost any process which features a SCADA system for supervisory control. When running in on-line mode, the process is monitored by a software agent known as the Supervisor Agent. FAST has also the capability of implementing distributed FDI using its multi-agent architecture. The tool is able to partition complex industrial processes into subsystems, identify which process variables need to be shared by each subsystem and instantiate a Supervision Agent for each of the partitioned subsystems. The Supervision Agents once instantiated will start diagnosing their local components and handle the requests to provide the variable values which FAST has identified as shared with other agents to support the distributed FDI process.Per tal de facilitar la utilització d'eines per la detecció i identificació de fallades (FDI) en la indústria, es requereix un enfocament sistemàtic per permetre als enginyers de processos analitzar un sistema des d'aquesta perspectiva. D'aquesta forma, hauria de ser possible analitzar aquest sistema per determinar si proporciona el diagnosi de fallades i la redundància d'acord amb la seva criticitat. A més, hauria de ser possible avaluar escenaris de casos modificant lleugerament el procés (per exemple afegint sensors o canviant la seva localització) i avaluant l'impacte en quant a les possibilitats de diagnosi de fallades i redundància. Per tant, aquest projecte proposa un enfocament per analitzar un procés des de la perspectiva FDI i per tal d'implementar-ho proporciona l'eina FAST la qual cobreix des de la fase d'anàlisi i disseny fins a la implementació final d'un supervisor FDI en un procés real. Per sintetitzar la informació del procés s'ha definit un format simple basat en XML. Aquest format proporciona la informació necessària per realitzar de forma sistemàtica l'Anàlisi Estructural del procés. Qualsevol procés pot ser analitzat, només hi ha la restricció de que els models dels components han d'estar disponibles en l'eina FAST. Els processos es descriuen en termes de variables de procés, components i relacions i l'eina realitza l'anàlisi estructural obtenint: (i) la matriu estructural, (ii) el Perfect Matching, (iii) les relacions de redundància analítica, si n'hi ha, i (iv) la matriu signatura de fallades. Per ajudar durant el procés d'anàlisi, FAST pot operar aïlladament en mode de simulació permetent a l'enginyer de procés avaluar fallades, la seva detectabilitat i implementar canvis en els components del procés i la topologia per tal de millorar les capacitats de diagnosi i redundància. Per altra banda, FAST pot operar en línia connectat al procés de la planta per mitjà d'una interfície OPC. La interfície OPC permet la possibilitat de connectar gairebé a qualsevol procés que inclogui un sistema SCADA per la seva supervisió. Quan funciona en mode en línia, el procés està monitoritzat per un agent software anomenat l'Agent Supervisor. Addicionalment, FAST té la capacitat d'implementar FDI de forma distribuïda utilitzant la seva arquitectura multi-agent. L'eina permet dividir sistemes industrials complexes en subsistemes, identificar quines variables de procés han de ser compartides per cada subsistema i generar una instància d'Agent Supervisor per cadascun dels subsistemes identificats. Els Agents Supervisor un cop activats, començaran diagnosticant els components locals i despatxant les peticions de valors per les variables que FAST ha identificat com compartides amb altres agents, per tal d'implementar el procés FDI de forma distribuïda.Postprint (published version

Data-driven fault diagnosis of awind farm benchmark model

The fault diagnosis of wind farms has been proven to be a challenging task, and motivates the research activities carried out through this work. Therefore, this paper deals with the fault diagnosis of a wind park benchmark model, and it considers viable solutions to the problem of earlier fault detection and isolation. The design of the fault indicator involves data-driven approaches, as they can represent effective tools for coping with poor analytical knowledge of the system dynamics, noise, uncertainty, and disturbances. In particular, the proposed data-driven solutions rely on fuzzy models and neural networks that are used to describe the strongly nonlinear relationships between measurement and faults. The chosen architectures rely on nonlinear autoregressive with exogenous input models, as they can represent the dynamic evolution of the system over time. The developed fault diagnosis schemes are tested by means of a high-fidelity benchmark model that simulates the normal and the faulty behaviour of a wind farm installation. The achieved performances are also compared with those of a model-based approach relying on nonlinear differential geometry tools. Finally, a Monte-Carlo analysis validates the robustness and reliability of the proposed solutions against typical parameter uncertainties and disturbances.The fault diagnosis of wind farms has been proven to be a challenging task, and motivates the research activities carried out through this work. Therefore, this paper deals with the fault diagnosis of a wind park benchmark model, and it considers viable solutions to the problem of earlier fault detection and isolation. The design of the fault indicator involves data-driven approaches, as they can represent effective tools for coping with poor analytical knowledge of the system dynamics, noise, uncertainty, and disturbances. In particular, the proposed data-driven solutions rely on fuzzy models and neural networks that are used to describe the strongly nonlinear relationships between measurement and faults. The chosen architectures rely on nonlinear autoregressive with exogenous input models, as they can represent the dynamic evolution of the system over time. The developed fault diagnosis schemes are tested by means of a high-fidelity benchmark model that simulates the normal and the faulty behaviour of a wind farm installation. The achieved performances are also compared with those of a model-based approach relying on nonlinear differential geometry tools. Finally, a Monte-Carlo analysis validates the robustness and reliability of the proposed solutions against typical parameter uncertainties and disturbances

Actuator fault estimation based on a switched LPV extended state observer

article en cours de soumission à une revueActuator fault estimation problem is tackled in this paper. The actuator faults are modeled in the form of multiplicative faults by using effectiveness factors representing the loss of efficiency of the actuators. The main contribution of this paper lies in the capability of dealing with the presented problem by using a switched LPV observer approach. The LTI system in the presence of faulty actuators is rewritten as a switched LPV system by considering the control inputs as scheduling parameters. Then, the actuator faults and the system states are estimated using a switched LPV extended observer. The observer gain is derived, based on the LMIs solution for the switched LPV systems. The presented actuator fault estimation approach is validated by two illustrative examples, the first one about a damper fault estimation of a semi-active suspension system, and the second one concerned to fault estimations on a multiple actuators system

A methodology for building a fault diagnoser for hybrid systems

In this paper, a design methodology for building diagnosers for hybrid systems is proposed. The design methodology uses as a starting point a hybrid automaton model to represent the hybrid system behaviour by means of the interaction of continuous dynamics and discrete events. Then, a hybrid fault diagnoser is designed using the methodology described in this paper and implemented by means of a discrete event system which carries out the mode recognition and diagnostic tasks, both based on residuals generated using models. Both tasks interact each other since the diagnosis module adapts according to the current mode of the hybrid system. The mode recognition task involves detecting and identifying the mode change by determining the set of residuals that are consistent with the current mode of the hybrid system. On the other hand, the diagnostic task involves detecting and isolating faults by identifying the fault that can explain the set of residuals that are inconsistent. A section of the Barcelona sewer network is used as application case study to illustrate the proposed fault diagnosis for hybrid systems.Peer ReviewedPostprint (author’s final draft

AI-based Diagnostics for Fault Detection and Isolation in Process Equipment Service

Recent industry requires efficient fault discovering and isolation solutions in process equipment service. This problem is a real-world problem of typically ill-defined systems, hard to model, with large-scale solution spaces. Design of precise models is impractical, too expensive, or often non-existent. Support service of equipment requires generating models that can analyze the equipment data, interpreting the past behavior and predicting the future one. These problems pose a challenge to traditional modeling techniques and represent a great opportunity for the application of AI-based methodologies, which enable us to deal with imprecise, uncertain data and incomplete domain knowledge typically encountered in real-world applications. In this paper the state of the art, theoretical background of conventional and AI-based techniques in support of service tasks and illustration of some applications to process equipment service on bio-ethanol production process are shortly described

observer and energy-balance based approaches

Due to the increasing complexity of modern technical processes, the most critical issues in the design of an automated system nowadays are safety/reliability, higher performance and cost efficiency. Faults in process components can lead to a considerable reduce of the efficiency of the process, quality of the product and in some cases even result in fatalities. In order to avert these losses, an efficient diagnosis of the faults plays a central role. Therefore, fault diagnosis is becoming an essential part of modern control systems. Fault diagnosis of linear dynamical systems has been extensively studied since decades and well-established techniques exist in the literature. However, fault diagnosis for nonlinear dynamical systems is yet an active field of research. Since most of real systems are nonlinear in nature, classically, linear fault diagnosis techniques have been applied to nonlinear systems based on the linearized system model around an operating point. The drawback of this approach is the limited fault diagnosis performance. In order to fulfill the increasing demand of more effective fault diagnosis systems for nonlinear processes, a lot of attention has been paid to nonlinear fault diagnosis techniques, which is the major topic of this thesis. Different from linear systems, there is no uniform solution for the fault diagnosis of general nonlinear systems. Various schemes have been proposed for nonlinear systems with special structures. Among them, Lipschitz nonlinear systems have been intensively studied, since on one hand more general nonlinear systems can be transformed into Lipschitz nonlinear systems, and on the other hand, many linear fault diagnosis approaches can be extended to this kind of nonlinear systems. For Lipschitz nonlinear systems, observer-based fault detection approach has been mostly applied, which consists of an observer-based residual generator and a residual evaluator. Classically, residual generator and residual evaluator are designed separately. Since the performance of fault detection system is decided by residual generator and evaluator together, it can be expected that, higher fault detection performance can be achieved by designing these two units in an integrated manner instead of separate handling of them. Motivated by this fact, an integrated design approach of observer-based residual generator and evaluator is proposed for Lipschitz nonlinear systems. Besides the schemes extended from linear methods (i.e. observer-based approach, parity space approach etc.), new nonlinear fault diagnosis techniques have also been studied recently, which can be effectively applied to complex nonlinear systems i.e. switched nonlinear systems, hybrid nonlinear systems etc. Among them, new fault diagnosis schemes based on passivity and energy-balance which are closely related to system “energy” have a great potential due to their clear physical meanings. In this thesis, this approach is extended to a complete fault detection and isolation framework with the focus on passive nonlinear systems. The fault diagnosis methodologies proposed in this thesis are tested with the design examples in the respective chapters and with the robot manipulator benchmark problem. The simulation results show the effectiveness of the proposed schemes.Aufgrund der zunehmenden Komplexität moderner technischer Verfahren sind heutzutage Sicherheit/Zuverlässigkeit, höhere Leistung und Kosteneffizienz wichtige Probleme bei der Gestaltung eines automatisierten Systems. Fehler in Prozesskomponenten führen zu einer erheblichen Reduzierung im Wirkungsgrad des Prozesses, in der Qualität des Produktes und können im schlimmsten Fall sogar katastrophale Folgen haben. Um dies zu vermeiden ist eine effiziente Diagnose der Fehler von zentraler Bedeutung. Fehlerdiagnose ist daher ein wesentlicher Bestandteil von modernen Steuerungssystemen geworden. Die Fehlerdiagnose bei linearen dynamischen Systemen wurde seit Jahrzehnten ausführlich untersucht und gut etablierte Techniken existieren in der Literatur, dagegen ist die Fehlerdiagnose für nichtlineare dynamische Systeme noch ein aktives Forschungsfeld. Da die meisten realen Systemen nichtlineare sind, werden lineare Fehlerdiagnosetechniken meistens auf ein linearisiertes Systemmodell angewendet, was sich jedoch nachteilig auf die Leistung auswirkt. Deshalb gewinnt nichtlineare Fehlerdiagnosetechnik zur Erfüllung der wachsenden Nachfrage nach einer besseren Fehlerdiagnose für nichtlineare Prozesse immer mehr an Bedeutung und ist daher das Hauptthema dieser Dissertation. Da es keine einheitliche Lösung für die Fehlerdiagnose allgemeiner nichtlinearer Systeme gibt werden bestimmte nichtlineare Systeme mit speziellen Strukturen untersucht. Unter ihnen sind besonders die Lipschitz Systeme intensiv untersucht worden, da einerseits viele allgemeine nichtlineare Systeme in Lipschitz Systeme umgewandelt werden können und andererseits viele lineare Fehlerdiagnose Ansätze für diese Art von nichtlinearen Systemen erweitert werden können. Für Lipschitz Systeme werden meist beobachtergestützte Fehlerdetektionsverfahren verwendet, die aus einem Residuengenerator und einer Residuenauswertung bestehen. Klassischerweise werden Residuengenerator und Residuenauswertung getrennt entworfen. Da die Leistung der Fehlerdetektion sowohl von Residuengenerator als auch von Residuenauswertung gemeinsam abhängt, ist zu erwarten, dass eine höhere Fehlererkennungsleistung erreicht werden kann, wenn der Entwurf dieser beiden Einheiten integriert erfolgt. Deshalb wird hier ein integrierter Design-Ansatz zur beobachtergestützten Fehlererkennung für Lipschitz Systeme vorgeschlagen. Neben der Erweiterung von linearen Methoden (beobachtergestützter Ansatz, Paritäts Raum Ansatz usw.) werden neue, nichtlineare Fehlerdiagnosetechniken seit kurzem untersucht, die auch auf komplexe, nichtlineare Systeme (geschaltete nichtlineare Systeme, hybride nichtlineare Systeme usw.) angewendet werden können. Unter ihnen besonders Passivitäts- und Energie-Bilanz- gestützte Verfahren, die eng mit der " Systemenergien" verbunden sind, ein großes Potenzial durch ihre klare physikalische Bedeutung. Diese Verfahren werden in dieser Dissertation zu einer vollständigen Fehlererkennungs- und Isolationsmethodik mit dem Fokus auf passive nichtlineare Systeme erweitert. Die gezeigten Algorithmen werden in den entsprechenden Kapiteln anhand von numerischen Beispielen getestet. Weiterhin wird die Verwendung der Algorithmen an dem geläufigen Beispielprozess eines Roboter Manipulators gezeigt um deren Nutzen und Anwendbarkeit zu demonstrieren