Bearings Fault Diagnosis based on GMM Model using Lyapunov Exponent Spectrum

Abstract

The scheme of the bearings fault diagnosis based on Lyapunov exponent spectrum is investigated in this paper. During experiments, it is clearly observed that the largest Lyapunov exponent can effectively implement the bearing fault detection, however it fails to accurately separate the ball and outer vibration signals in the bearing fault diagnosis applications. In order to solve this problem, the two-dimensional Lyapunov exponents are exploited as features for subsequent classification tasks. Experiments show that the proposed approach can effectively remove the noises and improve significantly the performance. Furthermore, to deal with the problem of difficultly obtaining abnormal samples in fault diagnosis, a novel approach based on GMM and Bayesian classifier is proposed in this paper. The performances of detectors with two-dimensional Lyapunov exponents, the large Lyapunov exponent and Lyapunov exponent spectrum entropy as classification features are compared in experiments later. The results demonstrate the effectiveness and improvement of the proposed approach. Finally, a comparison with other methods such as MLP demonstrates its excellent performance with some concluding remarks.