Semi-supervised Bearing Fault Diagnosis Using Improved Graph Attention Network under Time-varying Speeds

SHAO Haidong; YAN Shen; XIAO Yiming; LIU Yi

doi:10.11999/JEIT220303

Volume 45 Issue 5

May 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(5): 1550-1558

SHAO Haidong, YAN Shen, XIAO Yiming, LIU Yi. Semi-supervised Bearing Fault Diagnosis Using Improved Graph Attention Network under Time-varying Speeds[J]. Journal of Electronics & Information Technology, 2023, 45(5): 1550-1558. doi: 10.11999/JEIT220303

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SHAO Haidong, YAN Shen, XIAO Yiming, LIU Yi. Semi-supervised Bearing Fault Diagnosis Using Improved Graph Attention Network under Time-varying Speeds[J]. Journal of Electronics & Information Technology, 2023, 45(5): 1550-1558. doi: 10.11999/JEIT220303

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Semi-supervised Bearing Fault Diagnosis Using Improved Graph Attention Network under Time-varying Speeds

doi: 10.11999/JEIT220303

SHAO Haidong^{1, 2
,
,},
YAN Shen¹,
XIAO Yiming¹,
LIU Yi³

1.
College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China
2.
Shanghai Key Laboratory of Space Navigation and Positioning Techniques, Shanghai 200030, China
3.
National Rail Transit Advanced Equipment Innovation Center, Zhuzhou 412000, China

Funds: The National Key Research and Development of China (2020YFB1712100), The National Natural Science Foundation of China (51905160), The Natural Science Fund for Excellent Young Scholars of Hunan Province (2021JJ20017), The Opening Project of Shanghai Key Laboratory of Space Navigation and Positioning Techniques (202105)

Received Date: 2022-03-21
Rev Recd Date: 2022-06-22

Available Online: 2022-06-25

Publish Date: 2023-05-10

Abstract

Abstract

Recent researches on semi-supervised bearing fault diagnosis based on Graph Neural Network (GNN) still have some problems, such as insufficient label information mining and relatively ideal diagnosis scenarios. In engineering practice, bearings are often operated under time-varying speeds such as startup and shutdown, and fault label samples become increasingly expensive. In response to the above challenges, a new method called semi-supervised bearing fault diagnosis using improved Graph ATtention network (GAT) under time-varying speeds is proposed. Based on K-Nearest Neighbor (KNN) algorithm and Smoothing Assumption (SA), the pseudo-label propagation strategy is designed to spread the label information to the neighborhood samples with similar distribution along the edge, so that the label information hidden in the limited samples can be fully utilized. Each vibration spectrum sample is considered as a node, and a semi-supervised learning model based on node-level GAN is constructed to explore further representative bearing fault features through the attention mechanism. The proposed method is applied to analyze two sets of bearing fault experimental data under time-varying speed, and the results show that the proposed method is able to diagnose accurately different fault modes of bearings at low label rates of no more than 2%, which is better than other commonly used semi-supervised learning methods of GNN.
- Bearing fault diagnosis,
- Improved Graph ATtention network (GAT),
- Time-varying speeds,
- Semi-supervised learning,
- Low label rates

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References(25)

References

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