A method of sensor fault detection and identification

Abstract A method of Bayesian belief network (BBN)-based sensor fault detection and identification is presented. It is applicable to processes operating in transient or at steady-state. A single-sensor BBN model with adaptable nodes is used to handle cases in which process is in transient. The single-sensor BBN model is used as a building block to develop a multi-stage BBN model for all sensors in the process under consideration. In the context of BBN, conditional probability data represents correlation between process measurable variables. For a multi-stage BBN model, the conditional probability data should be available at each time instant during transient periods. This requires generating and processing a massive data bank that reduces computational efficiency. This paper presents a method that reduces the size of the required conditional probability data to one set. The method improves the computational efficiency without sacrificing detection and identification effectiveness. It is applicable to model- and data-driven techniques of generating conditional probability data. Therefore, there is no limitation on the source of process information. Through real-time operation and simulation of two processes, the application and performance of the proposed BBN method are shown. Detection and identification of different sensor fault types (bias, drift and noise) are presented. For one process, a first-principles model is used to generate the conditional probability data, while for the other, real-time process data (measurements) are used.

[1]  Alice M. Agogino,et al.  A methodology for intelligent sensor validation and fusion used in tracking and avoidance of objects for automated vehicles , 1995, Proceedings of 1995 American Control Conference - ACC'95.

[2]  K. Kroschel,et al.  Applying Bayesian networks to fault diagnosis , 1994, 1994 Proceedings of IEEE International Conference on Control and Applications.

[3]  Weihua Li,et al.  Isolation enhanced principal component analysis , 1999 .

[4]  K. A. Kosanovich,et al.  Monitoring Process Performance in Real-Time , 1992, 1992 American Control Conference.

[5]  Rolf Isermann,et al.  Trends in the Application of Model Based Fault Detection and Diagnosis of Technical Processes , 1996 .

[6]  Hugh F. Durrant-Whyte,et al.  The detection of faults in navigation systems: a frequency domain approach , 1998, Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146).

[7]  S. Joe Qin,et al.  Sensor validation and process fault diagnosis for FCC units under MPC feedback , 2001 .

[8]  Alan S. Willsky,et al.  F-8 DFBW sensor failure identification using analytic redundancy , 1977 .

[9]  Silvio Simani,et al.  Diagnosis techniques for sensor faults of industrial processes , 2000, IEEE Trans. Control. Syst. Technol..

[10]  David J. Spiegelhalter,et al.  Local computations with probabilities on graphical structures and their application to expert systems , 1990 .

[11]  Sunil Vadera,et al.  A Probabilistic Model for Sensor Validation , 1996, UAI.

[12]  Prodromos Daoutidis,et al.  OUTPUT FEEDBACK CONTROL OF NONMINIMUM-PHASE NONLINEAR PROCESSES , 1994 .

[13]  Mark A. Kramer,et al.  Comparison of belief networks and rule-based expert systems for fault diagnosis of chemical processes , 1993 .

[14]  S. Qin,et al.  Detection and identification of faulty sensors in dynamic processes , 2001 .

[15]  Barry M. Wise,et al.  The process chemometrics approach to process monitoring and fault detection , 1995 .

[16]  David Singer,et al.  Augmented Models for Statistical Fault Isolation in Complex Dynamic Systems , 1985, 1985 American Control Conference.

[17]  J. Gertler.  Fault detection and isolation using parity relations , 1997 .

[18]  Michael J. Piovoso,et al.  Probabilistic model for sensor fault detection and identification , 2003 .

[19]  Nugroho Iwan Santoso,et al.  Nuclear plant fault diagnosis using probabilistic reasoning , 1999, 1999 IEEE Power Engineering Society Summer Meeting. Conference Proceedings (Cat. No.99CH36364).

[20]  Jie Chen,et al.  Observer-based fault detection and isolation: robustness and applications , 1997 .

[21]  Ann E. Nicholson,et al.  Sensor Validation Using Dynamic Belief Networks , 1992, UAI.

[22]  A. Komori,et al.  Diagnosis of instrument fault , 1994, Conference Proceedings. 10th Anniversary. IMTC/94. Advanced Technologies in I & M. 1994 IEEE Instrumentation and Measurement Technolgy Conference (Cat. No.94CH3424-9).

[23]  Mark A. Kramer,et al.  An evolutionary programming approach to probabilistic model-based fault diagnosis of chemical processes , 1995 .

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

[25]  V. Walton,et al.  Detecting Instrument Malfunctions in Control Systems , 1975, IEEE Transactions on Aerospace and Electronic Systems.

[26]  Finn Verner Jensen,et al.  Introduction to Bayesian Networks , 2008, Innovations in Bayesian Networks.

[27]  E. Alcorta Garcia,et al.  A novel design of structured observer-based residuals for FDI , 1999, Proceedings of the 1999 American Control Conference (Cat. No. 99CH36251).

[28]  R. N. Claek.  Instrument Fault Detection , 1978 .

[29]  Weihua Li,et al.  Detection, identification, and reconstruction of faulty sensors with maximized sensitivity , 1999 .

[30]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[31]  Janos Gertler,et al.  Balance calculations through dynamic system modelling , 1973 .

[32]  P. R. Spina,et al.  Reliability in the determination of gas turbine operating state , 2000, Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187).

[33]  Janos J. Gertler,et al.  Analytical Redundancy Methods in Fault Detection and Isolation , 1991 .

[34]  R. K. Mehra,et al.  Correspondence item: An innovations approach to fault detection and diagnosis in dynamic systems , 1971 .

[35]  Frédéric Kratz,et al.  Detection, isolation, and identification of sensor faults in nuclear power plants , 1996, IEEE Trans. Control. Syst. Technol..

[36]  D. M. Himmelblau,et al.  Instrument fault detection in systems with uncertainties , 1982 .

[37]  Thomas E. Marlin,et al.  Multivariate statistical monitoring of process operating performance , 1991 .

[38]  Qiang Luo,et al.  Robust isolable models for failure diagnosis , 1989 .

[39]  Yale Zhang,et al.  Integrated monitoring solution to start-up and run-time operations for continuous casting , 2003 .

[40]  van Schrick.  A comparison of IFD schemes: a decision aid for designers , 1994 .

[41]  Jacob Rootenberg,et al.  The general cyclical allocation algorithm for packet-switched store-and-forward computer communication network design , 1982 .

[42]  Giovanni Betta,et al.  Instrument fault detection and isolation: state of the art and new research trends , 1998, IMTC/98 Conference Proceedings. IEEE Instrumentation and Measurement Technology Conference. Where Instrumentation is Going (Cat. No.98CH36222).

[43]  Michael J. Piovoso,et al.  Process data chemometrics , 1991 .