Wavelet kernel local fisher discriminant analysis with particle swarm optimization algorithm for bearing defect classification

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.