Improvements to Single-Fault Isolation Using Estimated Parameters

A method for finer fault isolation or localization in the model-based fault detection and isolation (FDI) paradigm is developed using parallely computed bond graph models. Many of the existing modelbased FDI methods are based on the evaluation of model consistency expressed in terms of analytical redundancy relations (ARR). These evaluations lead to residuals, and a number of sensors are to be installed in the plant to generate independent signatures needed for fault isolation. However, all the possible faults may not be isolable with the available instrumentation, and it is sometimes expensive or technically impossible to install necessary sensors in the plant to physically measure each and every state. In such situations, all component faults may not be uniquely isolated. However, a unique fault parameter subspace can be identified. One of the possible solutions, as proposed in this article, is to estimate parameters of that subspace from the ARR by assuming a single-fault hypothesis and then to incorporate the estimated values in separate models to run parallel with the plant during the fault. Thereafter, comparison of model behaviors leads to localization of the faulty parameters. This method is applied to an example system.

[1]  Pieter J. Mosterman,et al.  Diagnosis of continuous valued systems in transient operating regions , 1999, IEEE Trans. Syst. Man Cybern. Part A.

[2]  G. C. Tiao,et al.  Bayesian inference in statistical analysis , 1973 .

[3]  Paul M. Frank,et al.  Fault diagnosis in dynamic systems: theory and application , 1989 .

[4]  Ranjit Karmakar,et al.  Modeling and Simulation of Engineering Systems Through Bondgraphs , 1999 .

[5]  Marcel Staroswiecki,et al.  Model builder using functional and bond graph tools for FDI design , 2005 .

[6]  Marcel Staroswiecki,et al.  Analytical redundancy relations for fault detection and isolation in algebraic dynamic systems , 2001, Autom..

[7]  Paul M. Frank,et al.  Issues of Fault Diagnosis for Dynamic Systems , 2010, Springer London.

[8]  Michel Kinnaert,et al.  Robust fault detection based on observers for bilinear systems , 1999, Autom..

[9]  Dominique Sauter,et al.  Optimal thresholding for robust fault detection of uncertain systems , 2000 .

[10]  M. Staroswiecki.  QUANTITATIVE AND QUALITATIVE MODELS FOR FAULT DETECTION AND ISOLATION , 2000 .

[11]  Michel Kinnaert,et al.  Diagnosis and Fault-Tolerant Control , 2004, IEEE Transactions on Automatic Control.

[12]  Jie Chen,et al.  A REVIEW OF PARITY SPACE APPROACHES TO FAULT DIAGNOSIS , 1992 .

[13]  Raghunathan Rengaswamy,et al.  A review of process fault detection and diagnosis: Part III: Process history based methods , 2003, Comput. Chem. Eng..

[14]  Pieter J. Mosterman,et al.  Modeling and instrumentation for fault detection and isolation of a cooling system , 2000, Proceedings of the IEEE SoutheastCon 2000. 'Preparing for The New Millennium' (Cat. No.00CH37105).

[15]  Dan T. Horak.  Failure detection in dynamic systems with modeling errors , 1988 .

[16]  P. Frank.  Enhancement of Robustness in Observer-Based Fault Detection , 1991 .

[17]  Hassan Hammouri,et al.  Application of nonlinear observers to fault detection and isolation , 1999 .

[18]  Andrew Ball,et al.  The development of an adaptive threshold for model-based fault detection of a nonlinear electro-hydraulic system , 2005 .

[19]  M. M. Akhter,et al.  Effect of model uncertainty on failure detection: the threshold selector , 1988 .

[20]  Venkat Venkatasubramanian,et al.  Automatic generation of qualitative descriptions of process trends for fault detection and diagnosis , 1991 .

[21]  Vicenç Puig,et al.  Passive robust fault detection using fuzzy parity equations , 2002, Math. Comput. Simul..

[22]  Rolf Isermann,et al.  Process fault detection based on modeling and estimation methods - A survey , 1984, Autom..

[23]  Michèle Basseville,et al.  Detection of Abrupt Changes: Theory and Applications. , 1995 .

[24]  Ahmed Rahmani,et al.  Bond graph aided design of controlled systems , 1999, Simul. Pract. Theory.

[25]  P. Breedveld,et al.  Bond Graph Modeling Procedures for Fault Detection and Isolation of Complex Flow Processes , 2001 .

[26]  Paul M. Frank,et al.  Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy: A survey and some new results , 1990, Autom..

[27]  Mustapha Ouladsine,et al.  Fault detection in ARX process , 1993, Proceedings of IEEE Systems Man and Cybernetics Conference - SMC.

[28]  Steven X. Ding,et al.  Frequency domain approach to optimally robust residual generation and evaluation for model-based fault diagnosis , 1994, Autom..

[29]  R. Patton,et al.  Robust fault detection using eigenstructure assignment: a tutorial consideration and some new results , 1991, [1991] Proceedings of the 30th IEEE Conference on Decision and Control.

[30]  Kamal Medjaher,et al.  Diagnostic bond graphs for online fault detection and isolation , 2006, Simul. Model. Pract. Theory.

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

[32]  Marcel Staroswiecki,et al.  Supervision of an industrial steam generator. Part I: Bond graph modelling , 2006 .

[33]  P. Frank,et al.  Survey of robust residual generation and evaluation methods in observer-based fault detection systems , 1997 .

[34]  Silvio Simani,et al.  Model-based fault diagnosis in dynamic systems using identification techniques , 2003 .

[35]  Alan S. Willsky,et al.  A survey of design methods for failure detection in dynamic systems , 1976, Autom..

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

[37]  Raghunathan Rengaswamy,et al.  A review of process fault detection and diagnosis: Part I: Quantitative model-based methods , 2003, Comput. Chem. Eng..

[38]  Jie Chen,et al.  Robust Model-Based Fault Diagnosis for Dynamic Systems , 1998, The International Series on Asian Studies in Computer and Information Science.

[39]  Belkacem Ould Bouamama,et al.  Component-Based Modelling of Thermofluid Systems for Sensor Placement and Fault Detection , 2004, Simul..

[40]  Jie Chen,et al.  Design of unknown input observers and robust fault detection filters , 1996 .

[41]  Dominique Sauter,et al.  Robust fault detection in uncertain dynamic systems , 2000, Autom..

[42]  Raghunathan Rengaswamy,et al.  A review of process fault detection and diagnosis: Part II: Qualitative models and search strategies , 2003, Comput. Chem. Eng..

[43]  Marcel Staroswiecki,et al.  Supervision of an industrial steam generator. Part II: Online implementation , 2006 .

[44]  Alan S. Perelson,et al.  System Dynamics: A Unified Approach , 1976, IEEE Transactions on Systems, Man, and Cybernetics.

[45]  A. Willsky,et al.  Analytical redundancy and the design of robust failure detection systems , 1984 .

[46]  Pieter J. Mosterman,et al.  A Combined Qualitative/Quantitative Approach for Fault Isolation in Continuous Dynamic Systems , 2000 .

[47]  Vicenç Puig,et al.  Robust Fault Detection: Active Versus Passive Approaches , 2000 .

[48]  W. Hays.  Statistical theory. , 1968, Annual review of psychology.