Fault Detection Schemes for Continuous-Time Stochastic Dynamical Systems

In this paper the fault detection (FD) task in stochastic continuous-time dynamical systems is addressed. A new family of FD approaches is presented, which is based on the application of hypothesis testing on continuous-time estimators. The given FD schemes are widely analyzed in the framework of their characteristics, such as fault detectability, false alarms and missed detection. A collection of sufficient detectability conditions are given for a class of faults (referred here as generic), characterizing the faults which can be detected with certain formalized guarantee by the given FD schemes, and providing also an upper bound for the detection time in a probabilistic sense. The application and comparative performance of these FD approaches is illustrated for different faults in a simulation example.

[1]  Pedro J. Zufiria,et al.  Diagnosis of unknown parametric faults in non-linear stochastic dynamical systems , 2009, Int. J. Control.

[2]  J.J. Gertler,et al.  Survey of model-based failure detection and isolation in complex plants , 1988, IEEE Control Systems Magazine.

[3]  Xuehong Sun,et al.  Hybrid System State Tracking and Fault Detection Using Particle Filters , 2006, IEEE Transactions on Control Systems Technology.

[4]  Donghua Zhou,et al.  Fast and robust fault diagnosis for a class of nonlinear systems: detectability analysis , 2004, Comput. Chem. Eng..

[5]  Michèle Basseville,et al.  Fault Detection and Isolation in Nonlinear Dynamic Systems: A Combined Input-Output and Local Approach , 1998, Autom..

[6]  Hassan Hammouri,et al.  Observer-based approach to fault detection and isolation for nonlinear systems , 1999, IEEE Trans. Autom. Control..

[7]  Michel Loeve,et al.  Teoría de la probabilidad , 1976 .

[8]  P. Spreij.  Probability and Measure , 1996 .

[9]  Michèle Basseville,et al.  Model-based statistical signal processing and decision theoretic approaches to monitoring , 2003 .

[10]  Alessandro De Luca,et al.  Relaxed fault detection and isolation: An application to a nonlinear case study , 2006, Autom..

[11]  Janos Gertler,et al.  Fault detection and diagnosis in engineering systems , 1998 .

[12]  Marios M. Polycarpou,et al.  Fault Detection and Isolation Scheme in Continuous Time Nonlinear Stochastic Dynamical Systems , 2003 .

[13]  Visakan Kadirkamanathan,et al.  Fault detection and isolation in non-linear stochastic systems—A combined adaptive Monte Carlo filtering and likelihood ratio approach , 2004 .

[14]  M. Polycarpou,et al.  Learning methodology for failure detection and accommodation , 1995 .

[15]  Marcel Staroswiecki,et al.  Fault Diagnosis for Nonlinear Uncertain Systems Using Robust/Sliding Mode Observers , 2005, Control. Intell. Syst..

[16]  Gildas Besançon,et al.  High-gain observation with disturbance attenuation and application to robust fault detection , 2003, Autom..

[17]  Michel Kinnaert,et al.  Fault diagnosis based on analytical models for linear and nonlinear systems - a tutorial , 2003 .

[18]  Marios M. Polycarpou,et al.  Sensor bias fault isolation in a class of nonlinear systems , 2005, IEEE Transactions on Automatic Control.

[19]  Marios M. Polycarpou,et al.  Learning approach to nonlinear fault diagnosis: detectability analysis , 2000, IEEE Trans. Autom. Control..

[20]  P. Frank,et al.  Deterministic nonlinear observer-based approaches to fault diagnosis: A survey , 1997 .

[21]  Vincent Cocquempot,et al.  Fault diagnosis using sliding mode observer for nonlinear systems , 2002 .

[22]  Frank Allgöwer,et al.  Diagnosis of parametric faults in multivariable nonlinear systems , 2007, 2007 46th IEEE Conference on Decision and Control.

[23]  Alberto Isidori,et al.  A geometric approach to nonlinear fault detection and isolation , 2000, IEEE Trans. Autom. Control..

[24]  Rolf Isermann,et al.  Fault-diagnosis systems : an introduction from fault detection to fault tolerance , 2006 .

[25]  Marios M. Polycarpou,et al.  Automated fault detection and accommodation: a learning systems approach , 1995, IEEE Trans. Syst. Man Cybern..

[26]  Michèle Basseville,et al.  Detection of abrupt changes: theory and application , 1993 .

[27]  Józef Korbicz,et al.  A BOUNDED-ERROR APPROACH TO DESIGNING UNKNOWN INPUT OBSERVERS , 2002 .

[28]  Paul M. Frank,et al.  Analytical and Qualitative Model-based Fault Diagnosis - A Survey and Some New Results , 1996, Eur. J. Control.

[29]  C.Y. Chong,et al.  Probabilistic models in engineering sciences , 1981, Proceedings of the IEEE.

[30]  J. Lunze,et al.  Diagnosis of Complex Systems: Bridging the Methodologies of the FDI and DX Communities , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[31]  Qinghua Zhang,et al.  Nonlinear system fault detection and isolation based on bootstrap particle filters , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[32]  Qinghua Zhang,et al.  Nonlinear system fault diagnosis based on adaptive estimation , 2004, Autom..

[33]  Marcel Staroswiecki,et al.  The fault detection problem for dynamic systems: general results , 2003 .

[34]  Marios M. Polycarpou,et al.  Automated fault diagnosis in nonlinear multivariable systems using a learning methodology , 2000, IEEE Trans. Neural Networks Learn. Syst..

[35]  Leandro Pardo Llorente,et al.  Teoría de la probabilidad , 1997 .

[36]  Marios M. Polycarpou,et al.  A robust detection and isolation scheme for abrupt and incipient faults in nonlinear systems , 2002, IEEE Trans. Autom. Control..

[37]  Marios M. Polycarpou,et al.  Adaptive fault-tolerant control of nonlinear uncertain systems: an information-based diagnostic approach , 2004, IEEE Transactions on Automatic Control.