Unsupervised cross-domain rolling bearing fault diagnosis based on time-frequency information fusion
In recent years, data-driven methods have been widely used in rolling bearing fault diagnosis with great success, which mainly relies on the same data distribution and massive labeled data. However, bearing equipment is in normal working state for most of the time and operates under variable operati...
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| Published in | Journal of the Franklin Institute Vol. 360; no. 2; pp. 1454 - 1477 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Inc
01.01.2023
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| Online Access | Get full text |
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| Abstract | In recent years, data-driven methods have been widely used in rolling bearing fault diagnosis with great success, which mainly relies on the same data distribution and massive labeled data. However, bearing equipment is in normal working state for most of the time and operates under variable operating conditions. This makes it difficult to obtain bearing data labels, and the distribution of the collected samples varies widely. To address these problems, an unsupervised cross-domain fault diagnosis method based on time-frequency information fusion is proposed in this paper. Firstly, wavelet packet decomposition and reconstruction are carried out on the bearing vibration signal, and the energy eigenvectors of each sub-band are extracted to obtain a 2-D time-frequency map of fault features. Secondly, an unsupervised cross-domain fault diagnosis model is constructed, the improved maximum mean discrepancy algorithm is used as the measurement standard, and the joint distribution distance is calculated with the help of pseudo-labels to reduce data distribution differences. Finally, the model is applied to the motor bearing for comparison and verification. The results demonstrate its high diagnosis accuracy and strong robustness. |
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| AbstractList | In recent years, data-driven methods have been widely used in rolling bearing fault diagnosis with great success, which mainly relies on the same data distribution and massive labeled data. However, bearing equipment is in normal working state for most of the time and operates under variable operating conditions. This makes it difficult to obtain bearing data labels, and the distribution of the collected samples varies widely. To address these problems, an unsupervised cross-domain fault diagnosis method based on time-frequency information fusion is proposed in this paper. Firstly, wavelet packet decomposition and reconstruction are carried out on the bearing vibration signal, and the energy eigenvectors of each sub-band are extracted to obtain a 2-D time-frequency map of fault features. Secondly, an unsupervised cross-domain fault diagnosis model is constructed, the improved maximum mean discrepancy algorithm is used as the measurement standard, and the joint distribution distance is calculated with the help of pseudo-labels to reduce data distribution differences. Finally, the model is applied to the motor bearing for comparison and verification. The results demonstrate its high diagnosis accuracy and strong robustness. |
| Author | Chen, Yiyang Cheng, Long Qiu, Jier Tao, Hongfeng Stojanovic, Vladimir |
| Author_xml | – sequence: 1 givenname: Hongfeng surname: Tao fullname: Tao, Hongfeng email: taohongfeng@jiangnan.edu.cn organization: Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education, Jiangnan University, Wuxi 214122, P.R. China – sequence: 2 givenname: Jier surname: Qiu fullname: Qiu, Jier email: 1070416230@vip.jiangnan.edu.cn organization: Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education, Jiangnan University, Wuxi 214122, P.R. China – sequence: 3 givenname: Yiyang surname: Chen fullname: Chen, Yiyang email: yiyang.chen@soton.ac.uk organization: Department of Civil, Maritime and Environmental Engineering, University of Southampton, Southampton, SO16 7QF, United Kingdom – sequence: 4 givenname: Vladimir surname: Stojanovic fullname: Stojanovic, Vladimir email: vladostojanovic@mts.rs organization: Faculty of Mechanical and Civil Engineering,Department of Automatic Control, Robotics and Fluid Technique, University of Kragujevac, 36000 Kraljevo, Serbia – sequence: 5 givenname: Long surname: Cheng fullname: Cheng, Long email: 13032212876@163.com organization: Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education, Jiangnan University, Wuxi 214122, P.R. China |
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