By Topic

A hidden Markov model-based algorithm for online fault diagnosis with partial and imperfect tests

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
$33 $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)
J. Ying ; Dept. of Electr. & Syst. Eng., Connecticut Univ., Storrs, CT, USA ; T. Kirubarajan ; K. R. Pattipati

Presents a hidden Markov model (HMM) based algorithm for online fault diagnosis in complex large-scale systems with partial and imperfect tests. The HMM-based algorithm handles test uncertainties and inaccuracies, finds the best estimate of system states and identifies the dynamic changes in system states, such as from a fault-free state to a faulty one. We also present two methods to estimate the model parameters, namely the state transition probabilities and the instantaneous probabilities of observed test outcomes, for adaptive fault diagnosis. In order to validate the adaptive parameter estimation techniques, we present simulation results with and without the knowledge of HMM parameters. In addition, the advantages of using the HMM approach over a Hamming-distance based fault diagnosis technique are quantified. Tradeoffs in complexity versus performance of the diagnostic algorithm are discussed

Published in:

Soft Computing Methods in Industrial Applications, 1999. SMCia/99. Proceedings of the 1999 IEEE Midnight-Sun Workshop on

Date of Conference: