Please use this identifier to cite or link to this item:
Type: Artigo de periódico
Title: Adaptive fault detection and diagnosis using an evolving fuzzy classifier
Author: Lemos, A
Caminhas, W
Gomide, F
Abstract: This paper suggests an approach for adaptive fault detection and diagnosis. The proposed approach detects new operation modes of a process such as operation point changes and faults, and incorporates information about operation modes in an evolving fuzzy classifier used for diagnosis. The approach relies upon an incremental clustering procedure to generate fuzzy rules describing new operational states detected. The classifier performs diagnostic adaptively and, since every new operation mode detected is learnt and incorporated into the classifier, it is capable of identifying the same operation mode the next time it occurs. The efficiency of the approach is verified in fault detection and diagnosis of an industrial actuator. Experimental results suggest that the approach is a promising alternative for fault diagnosis of dynamic systems when there is no a priori information about all failure modes, and as an alternative to incremental learning of diagnosis systems using data streams. (C) 2011 Elsevier Inc. All rights reserved.
Subject: Evolving fuzzy systems
Participatory learning
Adaptive fault detection and diagnosis
Country: EUA
Editor: Elsevier Science Inc
Rights: fechado
Identifier DOI: 10.1016/j.ins.2011.08.030
Date Issue: 2013
Appears in Collections:Unicamp - Artigos e Outros Documentos

Files in This Item:
There are no files associated with this item.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.