An intelligent fault diagnosis method for roller bearings using an adaptive interactive deviation matrix machine

Abstract

Abstract Support matrix machines (SMMs) take a matrix as the modeled element and can fully mine the structural information of matrix samples. However, relying solely on a pair of parallel hyperplanes limits the performance of SMMs in classifying complex data. Therefore, this paper proposes an adaptive interactive deviation matrix machine (AIDMM). In the AIDMM, a sensitive margin parameter is introduced to construct two deviation hyperplanes, so that the parameter margin between the two deviation hyperplanes becomes flexible. Compared to the original fixed maximum-margin method, the parameter-margin AIDMM can better adjust the boundary of the deviation hyperplane according to the data, which contributes to improving insensitivity to noise and enhancing robustness. In addition, a multi-rank projection matrix is introduced to obtain a low-rank solution, which gives AIDMM a better fitting ability and avoids the problem of large training errors. Two roller bearing fault datasets are applied for experimental verification, and the experimental results show that AIDMM has excellent classification performance in roller bearing fault diagnosis.