Adaptive fault detection and diagnosis using an evolving fuzzy classifier

  • Authors:

  • Andre Lemos;Walmir Caminhas;Fernando Gomide

  • Affiliations:

  • Department of Electronic Engineering, Federal University of Minas Gerais, Belo Horizonte 31270-010, Brazil;Department of Electronic Engineering, Federal University of Minas Gerais, Belo Horizonte 31270-010, Brazil;School of Electrical and Computer Engineering, University of Campinas, Campinas 13083-852, Brazil

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2013

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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.