By Topic

A fault detection and diagnosis approach based on hidden Markov chain model

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Youmin Zhang ; Northwestern Polytech. Univ., Xian, China ; Li, X.R. ; Kemin Zhou

A fault detection and diagnosis (FDD) approach based on a hidden Markov chain model is proposed. In the proposed approach, the occurrence or recovery of a failure in a dynamic system is modeled as a finite-state Markov (or semi-Markov) chain with known transition probabilities. For such a hybrid system, either the interacting multiple-model (IMM) or the first-order generalized pseudo-Bayesian (GPB1) estimation algorithm can be used for state estimation, fault detection and diagnosis. The superiority of the approach is illustrated by an aircraft example for sensors and actuators failures. Both deterministic and random fault scenarios are designed and used for evaluating and comparing the performance. Some performance indices are presented. The robustness of the proposed approach to the design of model transition probabilities, fault modeling errors, and the uncertainties of noise statistics are also evaluated

Published in:

American Control Conference, 1998. Proceedings of the 1998  (Volume:4 )

Date of Conference:

21-26 Jun 1998