Monte Carlo simulation for model-based fault diagnosis in dynamic systems

Reliability Engineering and System Safety

Volumn 94, Issue 2, 2009, Pages 180-186

  Marseguerra, Marzio ^a   Zio, Enrico ^a  

^a POLITECNICO DI MILANO   (Italy)

Author keywords

Dynamic systems; Fault diagnosis; Monte Carlo; Particle filtering; Tank control system

Indexed keywords

ACOUSTIC SIGNAL PROCESSING; CONTROL SYSTEMS; DYNAMIC PROGRAMMING; ELECTRIC FAULT CURRENTS; MAXIMUM LIKELIHOOD ESTIMATION; MODAL ANALYSIS; NONLINEAR FILTERING; PROBABILITY DISTRIBUTIONS; RANDOM PROCESSES; RISK ASSESSMENT; SAMPLING; SIGNAL FILTERING AND PREDICTION; WAVE FILTERS;

DYNAMIC SYSTEMS; FAULT DIAGNOSIS; MONTE CARLO; PARTICLE FILTERING; TANK CONTROL SYSTEM;

MONTE CARLO METHODS;

EID: 54049089235     PISSN: 09518320     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ress.2008.02.013     Document Type: Article

References (20)

1. Isermann R, Balle P. Trends in the application of model-based fault detection and diagnosis of technical processes. In: Proceedings of the 13th IFAC triennial world congress, San Francisco, 1996. p. 2-12.
2. Wang P., Chen X.M., and Aldemir T. DSD: a generic software package for model-based fault diagnosis in dynamic systems. Reliab Eng Syst Saf 75 (2002) 31-39
3. Anderson B.D., and Moore J.B. Optimal filtering (1979), Prentice-Hall, Englewood Cliffs, NJ
4. Doucet A. On sequential simulation-based methods for bayesian filtering. Technical report, Department of Engineering, University of Cambridge, CUED-F-ENG-TR310. 1998.
5. Doucet A., de Freitas J.F.G., and Gordon N.J. An introduction to sequential Monte Carlo methods. In: Doucet A., de Freitas J.F.G., and Gordon N.J. (Eds). Sequential Monte Carlo in practice (2001), Springer, New York
6. Seong S.-H., Park H.-Y., Kim D.-H., Suh Y.-S., Hur S., Koo I.-S., et al. Development of fast running simulation methodology using neural networks for load follow operation. Nucl Sci Eng 141 (2002) 66-77
7. Kitagawa G. Non-Gaussian state-space modeling of nonstationary time series. J Am Stat Assoc 82 (1987) 1032-1063
8. Doucet A., Godsill S., and Andreu C. On sequential Monte Carlo sampling methods for Bayesian filtering. Stat Comput 10 (2000) 197-208
9. Djuric P.M., Kotecha J.H., Zhang J., Huang Y., Ghirmai T., Bugallo M.F., et al. Particle filtering. IEEE Signal Process Mag (2003) 19-37
10. In: Aldemir T., Siu N., Mosleh A., Cacciabue P.C., and Goktepe B.G. (Eds). Reliability and safety assessment of dynamic process systems, NATO-ASI Series F vol. 120 (1994), Springer, Berlin
11. Siu N. Risk assessment for dynamic systems: an overview. Reliab Eng Syst Saf 43 (1994) 43-74
12. Izquierdo J.M., Hortal J., Sanchez-Perea M., and Melendez E. Automatic generation of dynamic event trees: a tool for integrated safety assessment (ISA), reliability and safety assessment of dynamic process systems, NATO-ASI Series F vol. 120 (1994), Springer, Berlin
13. Labeau P.E. Probabilistic dynamics: estimation of generalized unreliability through efficient Monte Carlo simulation. Ann Nucl Energy 23 17 (1996) 1355-1369
14. Marseguerra M., and Zio E. Monte Carlo approach to PSA for dynamic process systems. Reliab Eng Syst Saf 52 (1996) 227-241
15. Labeau P.E., and Zio E. The cell-to-boundary method in the frame of memorization-based Monte Carlo algorithms. A new computational improvement in dynamic reliability. Math Comput Simulat 47 2-5 (1998) 329-347
16. Kalos M.H., and Whitlock P.A. Monte Carlo methods. Vol. Ι: Basics (1986), Wiley
17. Arulampalam M.S., Maskell S., Gordon N., and Clapp T. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Trans Signal Process 50 2 (2002) 174-188
18. Efron B. Bootstrap methods: another look at the jacknife. Ann Stat 7 (1979) 1-26
19. Efron B., and Tibshirani R.J. An Introduction to the bootstrap (1993), Chapman & Hall, New York
20. Gordon N.J., Salmond D.J., and Smith A.F.M. Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEEE Proc F 140 2 (1993) 107-113