Bearing fault diagnosis based on SVD feature extraction and transfer learning classification

This paper presents a transfer learning-based approach for bearing fault diagnosis, where the transfer strategy is proposed to improve diagnostic performance of the bearings under various operating conditions. The main idea of transfer learning is to utilize selective auxiliary data to assist target data classification, where a weight adjustment between them is involved in the TrAdaBoost algorithm for enhanced diagnostic capability. In addition, negative transfer is avoided through the similarity judgment, thus improving accuracy and relaxing computational load of the presented approach. Experimental comparison between transfer learning and traditional machine learning has verified the superiority of the proposed algorithm for bearing fault diagnosis.