Learning approach to nonlinear fault diagnosis: detectability analysis

The learning approach to fault diagnosis provides a methodology for designing monitoring architectures which can be used for detection, identification and accommodation of failures in dynamical systems. This paper considers the issues of detectability conditions and detection time in a nonlinear fau...

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Bibliographic Details
Published inIEEE transactions on automatic control Vol. 45; no. 4; pp. 806 - 812
Main Authors Polycarpou, M.M., Trunov, A.B.
Format Journal Article
LanguageEnglish
Published New York IEEE 01.04.2000
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Accommodation
Condition monitoring
Design methodology
Design parameters
Dynamical systems
Failure analysis
Fault detection
Fault diagnosis
Faults
Information processing
Learning
Learning systems
Monitoring
Nonlinearity
Performance analysis
Redundancy
Upper bound
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Summary:The learning approach to fault diagnosis provides a methodology for designing monitoring architectures which can be used for detection, identification and accommodation of failures in dynamical systems. This paper considers the issues of detectability conditions and detection time in a nonlinear fault diagnosis scheme based on the learning approach. First, conditions are derived to characterize the range of detectable faults. Then, nonconservative upper bounds are computed for the detection time of incipient and abrupt faults. It is shown that the detection time bound decreases monotonically as the values of certain design parameters increase. The theoretical results are illustrated by a simulation example of a second-order system.
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ISSN:0018-9286
1558-2523
DOI:10.1109/9.847127