Particle filtering based multiple-model approach to fault diagnosis in nonlinear stochastic systems

2001 European Control Conference, ECC 2001

Volumn , Issue , 2001, Pages 1378-1383

  Li, Ping ^a   Kadirkamanathan, Visakan ^a  

^a UNIVERSITY OF SHEFFIELD   (United Kingdom)

Author keywords

Statistical approaches to fault diagnosis Nonlinear systems Stochastic systems Particle filters Bayes estimation

Indexed keywords

FAILURE ANALYSIS; KALMAN FILTERS; MONTE CARLO METHODS; STOCHASTIC CONTROL SYSTEMS; STOCHASTIC MODELS; STOCHASTIC SYSTEMS;

FAULT DETECTION AND ISOLATION; MONTE CARLO TECHNIQUES; MULTIPLE-MODEL APPROACHES; NON-LINEAR STOCHASTIC SYSTEMS; PARTICLE FILTER; PARTICLE FILTERING; STATE ESTIMATION METHODS; UNIVARIATE MODELS;

FAULT DETECTION;

EID: 84947442408     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.23919/ecc.2001.7076109     Document Type: Conference Paper

References (29)

1. B. D. O. Anderson and J. B. Moore. Optimal Filtering. Prentice-hall, Englewood Cliffs, NJ, 1979.
2. M. Athans, D. Castanon, K-P, Dunn, C. S. Greene, W. H. Lee, N. R. Sandeal, Jr. and A. S. Willsky. The stochastic control of the F-8C aircraft using a multiple model adaptive control (MMAC) method Part I: Equilibrium Filght. IEEE Trans. on Automatic Control, AC-22(5), pp768-780. 1977.
3. M. Basseville. Statistical Approaches to Industrial Monitoring Problems - Fault Detection and Isolation. Proc. IFAC System Identification'97, pp413-432. Fukuoka, Japan. 1997.
4. M. Basseville. Detecting changes in signals and systems -A survey. Automatica, 24(3): pp309-326. 1988.
5. M. Basseville and A. Benveniste. Detection of abrupt changes in signals and dynamical systems. Lecture notes in control and information sciences, 77. Springer-Verlag, Berlin, Dec. 1985.
6. A. Doucet, S. Godsill and C. Ardrieu. On sequential Monte Carlo sampling methods for Bayesian filtering. Statistics and computing, 10:pp197-208. 2000.
7. S. G. Fabri and V. Kadirkamanathan. Functional adaptive control: An intelligent systems approach. Springer-Verlag, London. 2001.
8. P. M. Frank, S. X. Ding and B. Koppen-Seliger. Current developments in the theory of FDI. In Proc. IFAC Symposium SAFEPROCESS'2000, 1: pp17-28. Budapest, Hungary, 14-16 June, 2000.
9. P. M. Frank. Analytical and qualitative model-based fault diagnosis - A survey and some new results. European Journal of Control, 1(2): pp6-28, 1996.
10. P. M. Frank. Fault diagnosis in dynamic systems using analytical and knowledge based redundancy - A survey and new results. Automatica, 26: pp459-474, 1990.
11. J. J. Gertler. Survey of Model-Based Failure Detection and Isolation in Complex Plants. IEEE Control Systems Magazine, 8(6): pp3-11, Dec. 1988.
12. N. J. Gordon, D. J. Salmond and A. F. M. Smith. Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings-F, 140(2): 107-113, 1993.
13. R. Isermann. Process fault detection based on modeling and estimation methods - A survey. Automatica, 20: pp387-404, 1984.
14. V. Kadirkamanathan, P. Li, M. H. Jaward and S. G. Fabri. Fault detection in nonlinear systems: The particle filtering approach. In Proc. of UKACC International Conference on Control'2000, Cambridge, UK. 4-7 Sept. 2000.
15. V. Kadirkamanathan, P. Li, M. H. Jaward and S. G. Fabri. A sequential Monte Carlo filtering approach to fault detection and isolation in nonlinear systems. In Proc. IEEE conference on Decision and Control'2000, pp2159(1-6), Sydney, Australia. Dec, 2000.
16. G. Kitagawa. Monte Carlo filter and smoother for non-Gaussian nonlinear state space models. Journal of Computational and Graphical Statistics, 5(1): 1-25, 1996.
17. G. Kitagawa. Non-Gaussian state-space modeling of nonstationary time series (with discussion). Journal of the American Statistical Association, 82(400): 1032-1063, 1987.
18. D. G. Lainiotis. Partitioning: A unifying framework for adaptive system, I: Estimation. Proc. of IEEE 64(8): pp1126-1142. 1976.
19. R. K. Mehra and J. Peschon. An innovations approach to fault detection and diagnosis in dynamic systems. Automatica, 7: pp637-640, 1971.
20. R. J. Patton. Robust model-based fault diagnosis: The State of The Art Proc. of IFAC/IMACS Symposium SAFEPROCESS' 94, pp1-24. Espoo, Finland. June, 1994.
21. R. J. Patton, P. M. Frank and R. Clark. Fault diagnosis in dynamic systems - Theory and application. Prenticehall, UK, 1989.
22. A. D. Pouliezos and G. S. Stavrakakis. Real Time Fault Monitoring for Industrial Processes. Kluwer Academic Publishers, Netherlands, 1994.
23. H. W. Sorenson. Recursive estimation for nonlinear dynamic systems. in Bayesian Analysis of Time Series and Dynamic Models, edited by J. C. Spall. Marcel Dekker, Inc. New York, 1988.
24. P. B. Usoro, I. C. Schick and S. Negahdaripour. An innovation-based methodology for HVAC system fault detection. Journal of Dynamic Systems, Measurement, and Control. 107 pp284-289. Dec, 1985.
25. A. S. Willsky. Detection of abrupt changes in dynamic systems. in Lecture notes in control and information sciences, 77. Springer-Verlag, Berlin, Dec. 1985.
26. A. S. Willsky, E. Y. Chow, S. B. Gershwin, C. S. Greene, P. K. Houpt and A. L. Kurkjian. Dynamic model-based techniques for the detection of incidents on freeways. IEEE Trans. on Automatic Control, AC-25(3), June 1980.
27. A. S. Willsky. A survey of design methods for failure detection in dynamic systems. Automatica, 12: pp601-611, 1976.
28. Y. Zhang and X. R. Li. Detection and diagnosis of sensor and actuator failures using IMM estimator. IEEE Trans. on Aerospace and Electronic Systems, 34(4): pp. 1293-1313. Oct. 1998.
29. D. H. Zhou, Y. G. Xi and Z. J. Zhang. Nonlinear adaptive fault detection filter. Int. J. Systems Sci., 22: pp2563-2571, 1991.