Fault detection and isolation in stochastic non-linear state-space models using particle filters

Control Engineering Practice

Volumn 20, Issue 10, 2012, Pages 1016-1032

  Alrowaie, F ^a   Gopaluni, R B ^a   Kwok, K E ^a  

^a UNIVERSITY OF BRITISH COLUMBIA   (Canada)

Author keywords

Fault detection; Fault detection and isolation; Fault isolation; Particle filter

Indexed keywords

FAULT DETECTION AND ISOLATION; FAULT ISOLATION; HIDDEN STATE; HIGHLY NONLINEAR; LOG LIKELIHOOD RATIO; MODEL-BASED ALGORITHMS; MULTI-UNIT; PARTICLE FILTER; POLYETHYLENE REACTORS; PROCESS BEHAVIOR; RUNNING-IN; STATE ESTIMATORS; STATE-SPACE MODELS;

ALGORITHMS; CHEMICAL REACTORS; DISTRIBUTED COMPUTER SYSTEMS; FAULT DETECTION; NONLINEAR FILTERING; STOCHASTIC MODELS; STOCHASTIC SYSTEMS;

TARGET TRACKING;

EID: 84865350283     PISSN: 09670661     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.conengprac.2012.05.008     Document Type: Article

References (41)

1. Alrowaie, F., Kwok, K. E., & Gopaluni, R. B. (2011). An algorithm for fault detection in stochastic non-linear state-space models using particle filters. In International symposium on advanced control of industrial processes (ADCONIP) (pp. 60-65).
2. Arulampalam M., Maskell S., Gordon N. A tutorial on particle filters for online nonlinear/non-gaussian Bayesian tracking. IEEE Transactions on Signal Processing 2002, 50(2):174-188.
3. Basseville M. Detecting changes in signals and systems: A survey. Automatica 1988, 24(3):309-326.
4. Basseville M., Nikiforov I. Detection of abrupt changes: Theory and application 1993, Prentice-Hall, Englewood Cliffs, NJ.
5. Chen J., Patton R. Robust model-based fault diagnosis for dynamic systems 1999, Kluwer Academic Publishers, Boston.
6. Chow E., Willsky A. Analytical redundancy and the design of robust failure detection systems. IEEE Transactions on Automatic Control 1984, 29(7):603-614.
7. Dadebo S.A., Bell M.L., McLellan P.J., McAuley K.B. Temperature control of industrial gas phase polyethylene reactors. Journal of Process Control 1997, 7(2):83-95.
8. Dearden, R. & Clancy, D. (2002). Particle filters for real-time fault detection in planetary rovers. In Proceedings of the 13th international workshop on principles of diagnosis (pp. 1-6).
9. Doucet A., Godsill S., Andrieu C. On sequential monte carlo sampling methods for Bayesian filtering. Statistics and Computing 2000, 10(3):197-208.
10. El Farra N., Gani A., Christofides P. Fault-tolerant control of process systems using communication networks. International Journal of Systems Science 2005, 51(6):1665-1682.
11. Frank P. Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy-A survey and some new results. Automatica 1990, 26(3):459-474.
12. Frank P., Ding X. Survey of robust residual generation and evaluation methods in observer-based fault detection systems. Journal of Process Control 1997, 7(6):403-424.
13. Gandhi R., Mhaskar P. A safe-parking framework for plant-wide fault-tolerant control. Chemical Engineering Science 2009, 64(13):3060-3071.
14. Gani A., Mhaskar P., Christofides P.D. Fault-tolerant control of a polyethylene reactor. Journal of Process Control 2007, 17(5):439-451.
15. Gopaluni R.B. A particle filter approach to identification of nonlinear processes under missing observations. The Canadian Journal of Chemical Engineering 2008, 86(6):1081-1092.
16. Gopaluni R.B. Nonlinear system identification under missing observations: The case of unknown model structure. Journal of Process Control 2010, 20(3):314-324.
17. Gordon N., Salmond D., Smith A. Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEEE Proceedings on Radar and Signal Processing F 1993, 140(2):107-113.
18. Hutter, F. & Dearden, R. (2003). Efficient on-line fault diagnosis for non-linear systems. In Proceedings of the 7th international symposium on artificial intelligence and robotics in space.
19. Isermann R. Process fault detection based on modeling and estimation methods-A survey. Automatica 1984, 20(4):387-404.
20. Kadirkamanathan V., Li P. Fault detection and isolation in non-linear stochastic systems-A combined adaptive Monte Carlo filtering and likelihood ratio approach. International Journal of Systems Science 2004, 77(12):1101-1114.
21. Kadirkamanathan, V., Li, P., Jaward, M. H., & Fabri, S. (2000). A sequential monte carlo filtering approach to fault detection and isolation in nonlinear systems. In Proceedings of the 39th IEEE Conference on Decision and Control (Vol. 5, pp. 4341-4346).
22. Kadirkamanathan V., Li P., Jawardand M., Fabri S. Particle filtering-based fault detection in nonlinear stochastic systems. International Journal of Systems Science 2002, 33(4):259-265.
23. Koutsoukos, X., Kurien, J., & Zhao, F. (2002). Monitoring and diagnosis of hybrid systems using particle filtering methods. In Proceedings of the fifteenth international symposium on the mathematical theory of networks and systems (MTNS '02), University of Notre Dame, South.
24. Marseguerra M., Zio E. Monte Carlo simulation for model-based fault diagnosis in dynamic systems. Reliability Engineering and System Safety 2009, 94(2):180-186.
25. McAuley K.B., Macdonald D.A., McLellan P.J. Effects of operating conditions on stability of gas-phase polyethylene reactors. AIChE Journal 1995, 41(4):868-879.
26. Narasimhan, S., Dearden, R., & Benazera, E. (2004). Combining particle filters and consistency-based approaches for monitoring and diagnosis of stochastic hybrid systems. In Proceedings of 15th International workshop on principles of diagnosis.
27. Nimmo I. Adequately address abnormal situation operations. Chemical Engineering Progress 1995, 91(1):1361-1375.
28. Orchard, M. E. (2007). A particle filtering-based framework for on-line fault diagnosis and failure prognosis. Ph.D. thesis. Electrical and Computer Engineering. Georgia Institute of Technology, Atlanta.
29. Orchard M.E., Vachtsevanos G.J. A particle filtering approach for on-line failure prognosis in a planetary carrier plate. International Journal of Fuzzy Logic and Intelligent Systems 2007, 7(4):221-227.
30. Orchard, M. E. & Vachtsevanos, G. J. (2007b). A particle filtering-based framework for real-time fault diagnosis and failure prognosis in a turbine engine. In Proceedings of Mediterranean conference on control automation (pp. 1-6).
31. Orchard M.E., Vachtsevanos G.J. A particle-filtering approach for on-line fault diagnosis and failure prognosis. Transactions of the Institute of Measurement and Control 2009, 31(3-4):221-246.
32. Pouliezos A., Stavrakakis G. Real time fault monitoring of industrial processes 1994, Kluwer Academic Publishers, Norwell, MA, USA.
33. Roychoudhury, I. (2007). Distributed diagnosis of continuous systems: Global diagnosis through local analysis. Ph.D. thesis. Vanderbilt University. Nashville, Tennessee.
34. Roychoudhury, I., Biswas, G., & Koutsoukos, X. (2008). Comprehensive diagnosis of continuous systems using dynamic Bayes nets. In Proceedings of the 19th international workshop on principles of diagnosis (pp. 151-158).
35. Vedam H., Venkatasubramanian V. PCA-SDG based process monitoring and fault diagnosis. Control Engineering Practice 1999, 7(7):903-917.
36. Venkatasubramanian V., Rengaswamy R., Kavuri S. A review of process fault detection and diagnosis: Part ii: Qualitative models and search strategies. Computers & Chemical Engineering 2003, 27(3):313-326.
37. Venkatasubramanian V., Rengaswamy R., Yin K., Kavuri S. A review of process fault detection and diagnosis: Part i: Quantitative model-based methods. Computers & Chemical Engineering 2003, 27(3):293-311.
38. Verma, V. (2005). Tractable particle filters for robot fault diagnosis. Ph.D. thesis, Carnegie Mellon University.
39. Willsky A. A survey of design methods for failure detection in dynamic systems. Automatica 1976, 12(6):601-611.
40. Willsky A. Detection of abrupt changes in dynamic systems 1986, Springer-Verlag.
41. Yan B., Tian Z., Shi S. A novel distributed approach to robust fault detection and identification. International Journal of Electrical Power & Energy Systems 2008, 30(5):343-360.