Fuzzy multivariable Gaussian evolving approach for fault detection and diagnosis

Authors:
André Lemos;Walmir Caminhas;Fernando Gomide
Affiliations:
Department of Electrical Engineering, Federal University of Minas Gerais, MG, Brazil;Department of Electrical Engineering, Federal University of Minas Gerais, MG, Brazil;School of Electrical and Computer Engineering, University of Campinas, SP, Brazil
Venue:
IPMU'10 Proceedings of the Computational intelligence for knowledge-based systems design, and 13th international conference on Information processing and management of uncertainty
Year:
2010

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Abstract

This paper suggests an approach for fault detection and diagnosis capable to detect new operation modes online. The approach relies upon an evolving fuzzy classifier able to incorporate new operational information using an incremental unsupervised clustering procedure. The efficiency of the approach is verified in fault detection and diagnosis of an induction machine. 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. It is also attractive for incremental learning of diagnosis systems with streams of data.