Fault detection in dynamic systems based on fuzzy diagnosis

Author

Huallpa, Belisário Nina ; Nóbrega, Eurípedes ; Von Zuben, Fernando J.

Author_Institution

DMC-FEM-UNICAMP, Brazil

Volume

2

fYear

1998

fDate

4-9 May 1998

Firstpage

1482

Abstract

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

Keywords

Hopfield neural nets; directed graphs; fault diagnosis; pattern recognition; recursive estimation; state-space methods; Hopfield-type neural network; fault classification; fault detection; fuzzy diagnosis; fuzzy rules; recursive parameter estimation; signed directed graph; state-space model; time-invariant dynamic systems; Condition monitoring; Event detection; Fault detection; Fault diagnosis; Fuzzy systems; Neural networks; Parameter estimation; Pattern recognition; Process design; System identification;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems Proceedings, 1998. IEEE World Congress on Computational Intelligence., The 1998 IEEE International Conference on

Conference_Location

Anchorage, AK

ISSN

1098-7584

Print_ISBN

0-7803-4863-X

Type

conf

DOI

10.1109/FUZZY.1998.686338

Filename

686338