Feasibility Study of the GST-SVD in Extracting the Fault Feature of Rolling Bearing under Variable Conditions

Feature information extraction is one of the key steps in prognostics and health management of rotating machinery. In the present study, an investigation about the feasibility of a methodology based on generalized S transform (GST) and singular value decomposition (SVD) methods for feature extraction in rolling bearing, due to local damage under variable conditions, is conducted. The technique adopts the GST method, following the time-frequency analysis, to transform a raw fault signal of the rolling bearing into a two-dimensional complex matrix. And then, the SVD method is performed to decompose the matrix to obtain the feature vectors. By this procedure it is possible to obtain the fault feature information of rolling bearing under different speeds and different loads. In order to streamline the feature parameters of the feature vectors to train more uncomplicated models, the principal component analysis (PCA) subsequently performed. The particle swarm optimization-support vector machine (PSO-SVM) model is used to identify and classify the different fault states of rolling bearing. Furthermore, in order to highlight the superiority of the proposed method some comparisons are conducted with the conventional methods. The obtained results show that the proposed method can effectively extract fault features of the rolling bearing under variable conditions.