Symplectic interactive support matrix machine and its application in roller bearing condition monitoring

Abstract

Support matrix machine (SMM) is an effective method to solve the problem of mechanical condition monitoring while the matrix is taken as the input. It makes full use of the effective information between rows and columns of the matrix to establish an ideal prediction model and achieve good condition monitoring results. However, Similar to support vector machine (SVM), the core principle of the SMM is to distinguish the data effectively by two parallel hyperplanes. Unfortunately, two parallel hyperplanes may not be able to maximize the interval. Therefore, the concept of interactive support matrix machine (ISMM) is proposed, which constructs a pair of interactive hyperplanes to maximize the interval between two types of data. Interactive hyperplanes may be more able to distinguish between two types of data, so that each hyperplane is as close as to one of the two types and as far away as possible from the other. However, the input of the model often contain noise information, which seriously interferes with the classification results. Therefore, a symplectic interactive support matrix machine (SISMM) method is further proposed, which combines symplectic geometry similarity transformation (SGST) with ISMM. In SISMM, it can directly get the symplectic geometry coefficient matrix without noise from the original signal, and intelligent classification recognition is realized. By analyzing and comparing the signal of roller bearings, the results show that the proposed method has better recognition performance and it is feasible for roller bearing condition monitoring.