An evolving machinery fault diagnosis approach based on affinity propagation algorithm

Abstract

An evolving approach combining unsupervised clustering and supervised classification for intelligent machinery fault diagnosis is proposed. As the key point of the approach, the unsupervised clustering module for detecting a novel fault is specified in this paper. Incorporating with prior information in the historical data, a constrained clustering method is developed based on the affinity propagation (AP) algorithm. The clustering method is validated through experimental case studies on the bearing fault diagnosis. The results show that the clustering method has the self-learning abilities to detect the novel faults and with the evolving abilities of the proposed approach, one can start with just the normal condition data and continue building the diagnosis scheme as the new fault events occur. © 2010 Springer-Verlag.