A fault diagnosis approach for roller bearing based on symplectic geometry matrix machine

Abstract

In many classification problems such as roller bearing fault diagnosis, it is often met that input samples are two-dimensional matrices constructed by vibration signals, and the rows or columns in the input matrices are strongly correlated. Support matrix machine (SMM) is a new classifier with matrix as input, which has a good diagnostic effect by using of matrix structural information. Unfortunately, SMM algorithm is essentially binary, which need carry on the multiple binary classifications for multi-class classification problem. Meanwhile, SMM method has limitations in dealing with the complex input matrices, such as noise robustness and convergence problem. Therefore, a new classification method, called symplectic geometry matrix machine (SGMM), is proposed in this paper. In SGMM, by using symplectic geometry similarity transformation, the proposed method not only protects the original structure of the signal, but also automatically extracts noiseless features to establish weight coefficient model, which can achieve multi-class tasks. Meanwhile, because of establishment of weight coefficient model, the convergence problem can be avoided. The roller bearing fault signals are used to demonstrate the validity of the SGMM method, and the analysis results indicate that the proposed method has a good effectiveness in roller bearing fault diagnosis.