Symplectic weighted sparse support matrix machine for gear fault diagnosis

For gear fault diagnosis, it is often encountered that the input samples are naturally constructed as two-dimensional feature matrices with rich structure information. Support matrix machine (SMM) is an effective classifier for these matrix data, which fully leverages the matrix structure information. However, it is indispensable for SMM to artificially extract fault features and select the useful features, which requires plenty of professional knowledge. Hence, a symplectic weighted sparse SMM (SWSSMM) model is proposed in this paper. Under the concept of symplectic geometry, SWSSMM automatically extracts the symplectic weighted coefficient matrix (SWCM) as the fault feature representation. Meanwhile, the sparsity constraint and low-rank constraint are used in SWSSMM to eliminate the redundant fault features and capture the geometry structure information of SWCM, respectively. Besides, we derive an effective solver for SWSSMM with fast convergence. The experiment results demonstrate the superiority of SWSSMM for gear fault diagnosis.

Automated summary

This paper introduces a symplectic weighted sparse SMM (SWSSMM) model, which automatically extracts the symplectic weighted coefficient matrix (SWCM) as the fault feature representation, and utilizes sparsity constraint and low-rank constraint to eliminate redundant features and capture geometry structure information.