“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.

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:
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  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}