Wavelet Kernel Local Fisher Discriminant Analysis With Particle Swarm Optimization Algorithm for Bearing Defect Classification

Abstract

Feature extraction and dimensionality reduction (DR) are necessary and helpful preprocessing steps for bearing defect classification. Linear local Fisher discriminant analysis (LFDA) has recently been developed as a popular method for feature extraction and DR. However, the linear method tends to give undesired results if the samples between classes are nonlinearly separated in the input space. To enhance the performance of LFDA in bearing defect classification, a new feature extraction and DR algorithm based on wavelet kernel LFDA (WKLFDA) is presented in this paper. Herein, a new wavelet kernel function is proposed to construct the kernel function of LFDA. To seek the optimal parameters for WKLFDA, particle swarm optimization (PSO) is used; as a result, a new PSO-WKLFDA algorithm is proposed. The experimental results for the synthetic data and measured vibration bearing data show that the proposed WKLFDA and PSO-WKLFDA outperform other state-of-the-art algorithms.