A Multiclass Graph Embedding Matrix Classification Method for Roller Bearing State Identification Under Limited Sample

Support matrix machine (SMM) based methods have revolutionized the field of state identification by effectively mining correlations between fault features. However, some flaws limit its ability to handle interfered and limited samples, deriving from the purely focus on the closer samples nearing classify boundary and the thin design of binary classification nature, thus resulting SMM ignores the correlations between different samples and cannot align with the reality on the limited multiclass fault data. To address this issue, a novel approach called multiclass graph embedding support matrix machine (MGESMM) is proposed in this article. First, similarity matrix composed of similarity coefficient between each two samples are calculated by cosine distance. This similarity matrix is then used in manifold regularization-based graph embedding model, which can eliminate the negative impact of interfered and limited samples. Second, hamming loss-based predict error evaluation and multiclass loss-based boundary constraint is designed to form a direct multiclass classification constraint, thus the drawbacks of one-versus-one or one-versus-rest strategies for multiclass classification are prevented. Finally, to evaluate the efficacy of MGESMM, two roller bearing damage identification experiments are analyzed, and the results demonstrate that MGESMM achieves superior performance under different operating conditions.