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“A Unified Framework for Fault Detection and Diagnosis Using Particle Filter”

Authors: Bo Zhao and Roger Skjetne,
Affiliation: NTNU, Department of Marine Technology
Reference: 2014, Vol 35, No 4, pp. 303-315.

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Keywords: Fault detection and diagnosis, Particle filter, Hidden Markov model, Markov chain

Abstract: In this paper, a particle filter (PF) based fault detection and diagnosis framework is proposed. A system with possible faults is modeled as a group of hidden Markov models representing the system in fault-free mode and different failure modes, and a first order Markov chain is modeling the system mode transitions. A modified particle filter algorithm is developed to estimate the system states and mode. By doing this, system faults are detected when estimating the system mode, and the size of the fault is diagnosed by estimating the system state. A new resampling method is also developed for running the modified PF efficiently. Two introductory examples and a case study are given in detail. The introduction examples demonstrate the manner to model a system with possible faults into hidden Markov model and Markov chain. The case study considers a numerical model with common measurement failure modes. It focuses on the verification of the proposed fault diagnosis and detection algorithm and shows the behavior of the particle filter.

PDF PDF (836 Kb)        DOI: 10.4173/mic.2014.4.7

DOI forward links to this article:
  [1] Torleiv H. Bryne, Thor I. Fossen and Tor A. Johansen (2015), doi:10.1002/acs.2645
  [2] Torstein A. Myhre and Olav Egeland (2016), doi:10.1109/IECON.2016.7793396

[1] Arulampalam, M., Maskell, S., Gordon, N., and Clapp, T. (2002). A tutorial on particle filters for online nonlinear/non-gaussian bayesian tracking, Signal Processing, IEEE Transactions on. 50(2):174 --188. doi:10.1109/78.978374
[2] Blanke, M., Kinnaert, M., Lunze, J., and Staroswiecki, M. (2006). Diagnosis and Fault-Tolerant Control, Springer Berlin Heidelberg.
[3] Carpenter, J., Clifford, P., and Fearnhead, P. (1999). Improved particle filter for nonlinear problems, Radar, Sonar and Navigation, IEE Proceedings -. 146(1):2 --7. doi:10.1049/ip-rsn:19990255
[4] Chen, Z. (2003). Bayesian filtering: From kalman filters to particle filters, and beyond, Technical report, McMaster University.
[5] Doucet, A. and Johansen, A. (2009). The Oxford Handbook of Nonlinear Filtering, chapter A tutorial on particle filtering and smoothing: Fifteen years later, pages 1--39, December. Cambridge University Press, Cambridge.
[6] Doucet, A., Logothetis, A., and Krishnamurthy, V. (2000). Stochastic sampling algorithms for state estimation of jump markov linear systems, Automatic Control, IEEE Transactions on. 45(2):188 --202. doi:10.1109/9.839943
[7] Gordon, N., Salmond, D., and Smith, A. (1993). Novel approach to nonlinear/non-gaussian bayesian state estimation, Radar and Signal Processing, IEE Proceedings F. 140(2):107 --113. ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=210672.
[8] Gustafsson, F. (2001). Adaptive Filtering and Change Detection, John Wiley & Sons, Ltd, Chichester, UK, 1 edition.
[9] Isermann, R. (2006). Fault-diagnosis systems: an introduction from fault detection to fault tolerance, Springer Verlag, 1 edition.
[10] Kitagawa, G. (1996). Monte Carlo Filter and Smoother for Non-Gaussian Nonlinear State Space Models, Journal of Computational and Graphical Statistics. 5(1):pp. 1--25. doi:10.1080/10618600.1996.10474692
[11] Liu, J. (1996). Metropolized independent sampling with comparisons to rejection sampling and importance sampling, Statistics and Computing. 6:113--119. doi:10.1007/BF00162521
[12] Liu, J.S. and Chen, R. (1998). Sequential monte carlo methods for dynamic systems, Journal of the American Statistical Association. 93:1032--1044. doi:10.1080/01621459.1998.10473765
[13] Smith, A. F.M. and Gelfand, A.E. (1992). Bayesian statistics without tears: A Sampling-Resampling perspective, The American Statistician. 46(2):84--88. doi:10.1080/00031305.1992.10475856
[14] Zhao, B., Blanke, M., and Skjetne, R. (2012). Fault tolerant rov navigation system based on particle filter using hydroacoustic position and doppler velocity measurements, In 9th IFAC Conference on Manoeuvring and Control of Marine Craft. 2012. doi:10.3182/20120919-3-IT-2046.00048
[15] Zhao, B., Blanke, M., and Skjetne, R. (2012). Particle filter rov navigation using hydroacoustic position and speed log measurements, In American Control Conference (ACC), 2012. pages 6209 --6215, 2012. doi:10.1109/ACC.2012.6315511
[16] Zhao, B., Skjetne, R., Blanke, M., and Dukan, F. (2014). Particle filter for fault diagnosis and robust navigation of underwater robot, Control Systems Technology, IEEE Transactions on. 22(6):2399--2407. doi:10.1109/TCST.2014.2300815

  title={{A Unified Framework for Fault Detection and Diagnosis Using Particle Filter}},
  author={Zhao, Bo and Skjetne, Roger},
  journal={Modeling, Identification and Control},
  publisher={Norwegian Society of Automatic Control}


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