A classifier fusion system for bearing fault diagnosis

• A multiple classifier system is proposed to detect early defects on bearings.
• Different SVMs are combined using the Iterative Boolean Combination technique.
• The BEAring Toolbox is employed to produce a high amount of bearing vibration signals.
• The proposed strategy achieves high robustness to different noise-to-signal ratio.

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.