Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/100761
Type: Artigo de evento
Title: Fault Detection In Dynamic Systems Based On Fuzzy Diagnosis
Author: Huallpa Belisario Nina
Nobrega Euripedes
Von Zuben Fernando J.
Abstract: The detection and classification of faults in time-invariant dynamic systems involve tasks associated with system identification and pattern recognition. The purpose of this 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.
Editor: IEEE, Piscataway, NJ, United States
Rights: fechado
Identifier DOI: 
Address: http://www.scopus.com/inward/record.url?eid=2-s2.0-0031627858&partnerID=40&md5=6f8c176580dd0ac86463b289af00e72e
Date Issue: 1998
Appears in Collections:Unicamp - Artigos e Outros Documentos

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