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Bearing faults recognition based on the local mode decomposition morphological spectrum and optimized support vector machine

A novel method to solve the bearing fault recognition problem is proposed, which is based on local mode decomposition (LMD) to extract the characteristic features and the transfer learning strategy optimized support vector machine (TLSSVM) to achieve the fault state classification. Firstly, the gathered vibration signals were decomposed by the LMD to obtain the corresponding product functions (PFs). The morphological spectrums of the chosen PFs that include typical feature information are calculated and defined as the characteristic features. However, the extracted features remained high-dimensional, and excessive redundant information still existed. So, the principal components analysis (PCA) is used to extract the characteristic features to extract the characteristic features and reduce the dimension. The characteristic features are input into the SVM model, in order to get a higher accuracy of the diagnosis accuracy, because of the fuzzy features, the transfer learning strategy is used to optimized the SVM model, and the optimized model is used to construct the fault state identification model, the bearing fault state identification is realized. The fault states of a bearing normal and several inner races with different degree of fault were recognized, the results validate the effectiveness of the proposed algorithm.