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Hidden Markov Models for Fault Detection in Dynamic SystemsContinuous monitoring of complex dynamic systems is an increasingly important issue in diverse areas such as nuclear plant safety, production line reliability, and medical health monitoring systems. Recent advances in both sensor technology and computational capabilities have made on-line permanent monitoring much more feasible than it was in the past. In this paper it is shown that a pattern recognition system combined with a finite-state hidden Markov model provides a particularly useful method for modelling temporal context in continuous monitoring. The parameters of the Markov model are derived from gross failure statistics such as the mean time between failures. The model is validated on a real-world fault diagnosis problem and it is shown that Markov modelling in this context offers significant practical benefits.
Document ID
19990008600
Acquisition Source
Headquarters
Document Type
Reprint (Version printed in journal)
Authors
Smyth, Padhraic
(Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA United States)
Date Acquired
August 19, 2013
Publication Date
January 1, 1994
Publication Information
Publication: Pattern Recognition
Publisher: Elsevier Science Publishers Ltd.
Volume: 27
Issue: 1
Subject Category
Quality Assurance And Reliability
Funding Number(s)
CONTRACT_GRANT: AF-AFOSR-0199-90
CONTRACT_GRANT: N00014-92-J-1860
Distribution Limits
Public
Copyright
Other

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