Symplectic incremental matrix machine and its application in roller bearing condition monitoring

For roller bearing condition monitoring, the collected signals have complex internal structure, which can be naturally represented as matrices. Support matrix machine (SMM), as a new classifier with matrices as inputs, makes full use of the correlation between rows and columns of matrices and achieves ideal classification results. Unfortunately, SMM have ignored the issue of redundant features, which seriously affects the operational efficiency and recognition accuracy of algorithm. In this paper, we introduce symplectic geometry, l1-norm and incremental proximal descent (IPD) to SMM, and symplectic incremental matrix machine (SIMM) is proposed. In SIMM, through symplectic geometry similarity transformation, the de-noising symplectic geometry coefficient matrix is obtained, and the noise robustness of SMM method is therefore improved. Moreover, l1-norm is used to constrain the objective function, which can weaken the influence of redundant features, and thus greatly improving the recognition accuracy of SMM. Meanwhile, we use IPD to solve the objective function, which can obviously enhance the algorithm efficiency under the constant recognition rates. The experimental results of two kinds of roller bearings show that the proposed method has a good effectiveness in roller bearing condition monitoring, and the achieved recognition rate can reach 3%–25% much higher than those of the traditional recognition methods in 5-cross validation.