Detecting bearing defects under high noise levels: A classifier fusion approach

Automatic bearing fault diagnosis may be approached as a pattern recognition problem that allows for a significant reduction in the maintenance costs of rotating machines, as well as the early detection of potentially disastrous faults. When these systems employ real vibration data obtained from bearings artificially damaged, they have to cope with a very limited number of training samples. Moreover, an important issue that has been little investigated in the literature is the presence of noise, which disturbs the vibration signals, and how this affects the identification of bearing defects. 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. Experiments indicate that the proposed strategy can significantly reduce the error rates, even in the presence of very noisy signals.