A classifier fusion system for bearing fault diagnosis

Authors:
Luana Batista;Bechir Badri;Robert Sabourin;Marc Thomas
Affiliations:
-;-;-;-
Venue:
Expert Systems with Applications: An International Journal
Year:
2013

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Abstract

In this paper, a new strategy based on the fusion of different Support Vector Machines (SVM) is proposed in order to reduce noise effect in bearing fault diagnosis systems. Each SVM classifier is designed to deal with a specific noise configuration and, when combined together - by means of the Iterative Boolean Combination (IBC) technique - they provide high robustness to different noise-to-signal ratio. In order to produce a high amount of vibration signals, considering different defect dimensions and noise levels, the BEAring Toolbox (BEAT) is employed in this work. The experiments indicate that the proposed strategy can significantly reduce the error rates, even in the presence of very noisy signals.