An intelligent fault diagnosis method for roller bearing using symplectic hyperdisk matrix machine

Abstract

Support matrix machine (SMM) is a new and effective classification method, which has been applied in the field of image processing. In this paper, an improved SMM called symplectic hyperdisk matrix machine (SHMM) is proposed and applied to the roller bearing fault diagnosis. In SHMM, the symplectic geometry similarity transformation (SGST) is used to obtain the dimensionless feature matrix, which protects the signal structure information while weakening the interference of noise. Then, different types of hyperdisks are constructed to divide different types of data, several more realistic hyperdisk prediction models can be obtained and the problem of under estimation is avoided. In order to fully mine the spatial structure information, the feature matrix is mapped to the high-dimensional space by kernel technology, and the decision function is established by using the structure information hidden of the input matrix in the SHMM. Experimental results of three datasets of roller bearing show that, compared with symplectic geometry matrix machine (SGMM), SMM, support vector machine (SVM) and radial basis function (RBF) neural network methods, the proposed SHMM has good application effect in roller bearing fault diagnosis.