Ramp sparse support matrix machine and its application in roller bearing fault diagnosis

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As an efficient matrix classifier, support matrix machine (SMM) can make full use of the spatial structure of the input matrix and show superior diagnostic performance. However, the input feature matrix may be contaminated by noise to form some outliers, which will affect the classification accuracy due to excessive loss. Therefore, this paper proposes a new matrix classification method, called Ramp sparse support matrix machine (RSSMM). In RSSMM, it compulsorily limits a loss threshold under the Ramp loss function, which solves the problem of model generalization performance degradation caused by excessive loss. Meanwhile, the generalized forward–backward algorithm (GFB) is introduced into RSSMM as a solver, and a generalized smooth Ramp loss function is designed to solve the problem that the Ramp loss function itself does not have a continuous gradient. Two roller bearing fault data sets are used to prove the effectiveness of the RSSMM method, and the analysis results show the superiority of the proposed RSSMM method in the classification of roller bearing fault signal.