Reliable bearing fault diagnosis using Bayesian inference-based multi-class support vector machines

This paper proposes a highly reliable multi-fault diagnosis scheme for low-speed rolling element bearings using an effective time–frequency envelope analysis and a Bayesian inference based one-against-all support vector machines (probabilistic-OAASVM) classifier. The proposed method first performs a wavelet packet transform based envelope analysis on an acoustic emission signal to select sub-bands of the signal that contain the most intrinsic and pertinent information about the defects. Frequency- and time-domain fault features are extracted only from selected sub-bands for fault classification. Traditional one-against-all SVMs (OAASVM), a widely used multi-class pattern recognition technique, employ an arbitrary combination of a series of binary classifiers yielding overlapped feature spaces, where a data sample can be unclassifiable. To address this limitation, we formulate the feature space of OAASVM as an appropriate Gaussian process prior (GPP) and interpret OAASVM results as a posterior probability estimation procedure using Bayesian inference under this GPP. The efficacy of the proposed probabilistic-OAASVM classifier is verified for low-speed rolling element bearings under various conditions. Experimental results indicate that the proposed method outperforms the state-of-the-art algorithms for multi-fault classification of low-speed bearings, yielding a 4.95–20.67% improvement in the average classification accuracy.