Bearing defect identification via evolutionary algorithm with adaptive wavelet mutation strategy

Abstract

The vibration signals obtained from the bearings are associated with nonlinear and nonstationary characteristics, which hinder the fault identification of the bearing. A scheme of fault identification is proposed to identify the faults in bearing in this paper. The vibration signal first pre-processed by wavelet packet transform, followed by Hilbert-Huang transform, decomposes the reconstructed signal into the IMFs. The L-moment criterion is used to select a prominent IMF. The prominent IMF is further utilized to extract the features from the processed signal. The dataset prepared from features trains the SVM model. A novel optimization technique viz 'Diversity driven multi-parent evolutionary algorithm with adaptive wavelet mutation' is proposed to optimize the SVM parameters to increase its efficiency. The efficacy of the proposed algorithm is tested against benchmark functions; obtained results proved its effectiveness. The overall accuracy of built SVM is found to be 100% with suitable regularization and kernel parameter values.