Method for rolling bearing fault diagnosis under variable working conditions based on mixed noise dictionary and transfer subspace learning
ZHANG Jialing1,2,WU Jimei2,3
1. School of Mechanical and Material Engineering, Xi’an University, Xi’an 710065, China;
2.School of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an 710048, China;
3. School of Printing, Packaging Engineering and Digital Media Technology, Xi’an University of Technology, Xi’an 710054, China
Abstract:Rolling bearings are affected by complex working conditions in actual operation, which makes the vibration signals cannot satisfy the independent and identical distribution of training and test data. At the same time,the vibration signal is mixed with a large amount of noise and irrelevant information, which directly affect the bearing fault diagnosis ability. Therefore, a rolling bearing fault diagnosis method based on mixed noise dictionary and transfer subspace learning under variable working conditions is presented. First, a mixed noise dictionary model is constructed to remove the interference of irrelevant information components on dictionary learning. Then, a transfer subspace model is constructed to transfer the sparse signals into a common subspace. The distribution difference between the two domains is reduced by combining joint distribution adaptation and reducing the classification error of the source domain. Finally, an alternating direction method of multipliers is used to optimize the solution. Experimental results show that the proposed method can accurately identify rolling bearing fault types under complex variable working conditions.
Key words: rolling bearing; fault diagnosis; dictionary learning; transfer subspace learning
张嘉玲1,2,武吉梅2,3. 变工况下混合噪声字典和迁移子空间学习的滚动轴承故障诊断方法[J]. 振动与冲击, 2022, 41(18): 176-183.
ZHANG Jialing1,2,WU Jimei2,3. Method for rolling bearing fault diagnosis under variable working conditions based on mixed noise dictionary and transfer subspace learning. JOURNAL OF VIBRATION AND SHOCK, 2022, 41(18): 176-183.
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