ARLSSMM: an effective fault diagnosis method for roller bearing under strong noise

Support matrix machine (SMM) is a matrix classification method that effectively utilises information between rows and columns in matrix-form data. However, the presence of abnormal data in the feature matrix fed into the model can result in model non-convergence and hyperplane shift, greatly affecting classification accuracy. To address this issue, this paper introduces a matrix classification method, called asymmetric robust least squares support matrix machine (ARLSSMM). To construct ARLSSMM, a novel optimisation model based on a matrix with asymmetric constraints is designed to obtain the hyperplane, which can not only use the data set to dynamically determine the position of the hyperplane to obtain the maximum margin space but also reduce the influence of abnormal data on the model classification performance. Two different roller bearing types of test data are used for experimental validation, and the results show that ARLSSMM demonstrates excellent classification performance.