Fault detection in dynamic systems based on fuzzy diagnosis

The detection and classification of faults in time-invariant dynamic systems involve tasks associated with system identification and pattern recognition. The purpose of the paper is to present the design of a process of fault detection and classification. The faults are characterized by a permanent...

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Bibliographic Details
Main Authors: Huallpa, B.N., Nobrega, E., Von Zuben, F.J.
Format: Conference Proceeding
Language:English
Subjects:
Condition monitoring
Event detection
Fault detection
Fault diagnosis
Fuzzy systems
Neural networks
Parameter estimation
Pattern recognition
Process design
System identification
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Summary:The detection and classification of faults in time-invariant dynamic systems involve tasks associated with system identification and pattern recognition. The purpose of the paper is to present the design of a process of fault detection and classification. The faults are characterized by a permanent perturbation on physical parameters of the original system, an event that is detected by monitoring a state-space model of the system, subject to recursive parameter estimation. The main component of the estimation process is a Hopfield-type neural network. The evolution of the parameter values at the output of the parameter estimator is continuously analyzed and if their behavior matches some pattern of permanent perturbation, the process of fault diagnosis indicates the source of the fault. This is a pattern recognition problem, and its implementation is accomplished using fuzzy rules, designed from a signed directed graph.
ISSN:1098-7584
DOI:10.1109/FUZZY.1998.686338