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Rolling bearing fault diagnosis method based on Markov transition field and graph attention network
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Chun-li LEI1, 2, Lin-lin XUE1, 2, Ben-feng XIA1, 2, Meng-xuan JIAO1, 2, Jia-shuo SHI1, 2
Journal of Vibration Engineering | 2024, 37(12) : 2158 - 2167
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Journal of Vibration Engineering | 2024, 37(12): 2158-2167
Rolling bearing fault diagnosis method based on Markov transition field and graph attention network
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Chun-li LEI1, 2, Lin-lin XUE1, 2, Ben-feng XIA1, 2, Meng-xuan JIAO1, 2, Jia-shuo SHI1, 2
Affiliations
  • 1School of Mechanical and Electronical Engineering，Lanzhou University of Technology，Lanzhou 730050，China
  • 2Key Laboratory of Digital Manufacturing Technology and Application，Ministry of Education，Lanzhou University of Technology，Lanzhou 730050，China
Published: 2024-12-28 doi: 10.16385/j.cnki.issn.1004-4523.2024.12.018
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Aiming at the problem that the recognition accuracy of the model is not high due to the complex and variable engineering environment，a rolling bearing fault diagnosis model integrating Markov transition field and graph attention networks （MTF-GAT） is proposed in this paper. Using the advantage of MTF to retain the time correlation of the signal is applied to transform one-dimensional signals into two-dimensional feature maps，and the nodes and edges of the graph are defined. The graph attention layer can adaptively assign different weights to adjacent nodes to improve the ability of the model to capture useful fault features，and the abstract information of the graph is further extracted through the deep convolution module. By simulating the actual engineering environment，the various fault signals are input into the trained MTF-GAT model for fault diagnosis，and the model is verified by experiments on two data sets. The results show that the proposed model in this paper can accurately complete the task of fault classification in a variety of environments. Compared with other deep learning models，the MTF-GAT model has better recognition accuracy and generalization performance.

fault diagnosis  /  rolling bearings  /  graph attention networks  /  multi-head attention mechanism  /  Markov transition field
Chun-li LEI, Lin-lin XUE, Ben-feng XIA, Meng-xuan JIAO, Jia-shuo SHI. Rolling bearing fault diagnosis method based on Markov transition field and graph attention network[J]. Journal of Vibration Engineering, 2024 , 37 (12) : 2158 -2167 . DOI: 10.16385/j.cnki.issn.1004-4523.2024.12.018
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Year 2024 volume 37 Issue 12
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Article Info
doi: 10.16385/j.cnki.issn.1004-4523.2024.12.018
  • Receive Date：2022-12-27
  • Online Date：2026-02-12
  • Published：2024-12-28
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  • Received：2022-12-27
  • Revised：2023-03-03
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Affiliations
    1School of Mechanical and Electronical Engineering，Lanzhou University of Technology，Lanzhou 730050，China
    2Key Laboratory of Digital Manufacturing Technology and Application，Ministry of Education，Lanzhou University of Technology，Lanzhou 730050，China
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光柄菇属 Pluteus 5 2.39
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栓菌属 Trametes 5 2.39
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