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Multi-scale GraphSAGE with class center balancing loss for rolling bearing fault diagnosis under extremely class imbalance

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Abstract

The imbalance between normal and fault data in the condition monitoring of rotating machinery often leads to models needing more focus on the information from the majority class. To this end, this work proposed a rolling bearing fault diagnosis method based on class center balancing loss (CCBL) and multi-scale GraphSAGE (MSGraphSAGE) to handle extreme class imbalance. First, a node-level pathgraph using frequency-domain signals enhances the model’s learning and generalization capabilities by associating signal features. Next, a multi-scale feature extractor is designed, employing DropEdge-based MSGraphSAGE in the first layer to improve the model’s feature extraction performance. Finally, a CCBL function is developed to reweight the class weights, reducing the weight loss assigned to the majority class to balance the class weights. Six imbalanced cases were designed on two bearing datasets, and the experimental results demonstrate the advantages of this method in highly imbalanced fault diagnosis tasks, validating the effectiveness and superiority of the proposed GNN model and class center balancing loss.

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Data will be made available on request.

References

  1. Peng H, Zhang H, Fan Y, Shangguan L, Yang Y (2022) A review of research on wind turbine bearings’ failure analysis and fault diagnosis. Lubricants 11(1) https://doi.org/10.3390/lubricants11010014

  2. Xu Y, Li Z, Wang S, Li W, Sarkodie-Gyan T, Feng S (2021) A hybrid deep-learning model for fault diagnosis of rolling bearings. Measurement 169:108502. https://doi.org/10.1016/j.measurement.2020.108502

  3. Fernandes M, Corchado JM, Marreiros G (2022) Machine learning techniques applied to mechanical fault diagnosis and fault prognosis in the context of real industrial manufacturing use-cases: a systematic literature review. Appl Intell 52(12):14246–14280. https://doi.org/10.1007/s001090000086

    Article  Google Scholar 

  4. Ye M, Yan X, Jiang D, Xiang L, Chen N (2024) Mifdeln: A multi-sensor information fusion deep ensemble learning network for diagnosing bearing faults in noisy scenarios. Knowl-Based Syst 284:111294. https://doi.org/10.1016/j.knosys.2023.111294

  5. Yan X, Jiang D, Xiang L, Xu Y, Wang Y (2024) Cdtfafn: A novel coarse-to-fine dual-scale time-frequency attention fusion network for machinery vibro-acoustic fault diagnosis. Inf Fusion 112:102554. https://doi.org/10.1016/j.inffus.2024.102554

  6. Chen X, Yang R, Xue Y, Huang M, Ferrero R, Wang Z (2023) Deep transfer learning for bearing fault diagnosis: A systematic review since 2016. IEEE Trans Instrument Measure 72:1–21. https://doi.org/10.1109/TIM.2023.3244237

    Article  MATH  Google Scholar 

  7. Cheng Y, Zhu H, Wu J, Shao X (2019) Machine health monitoring using adaptive kernel spectral clustering and deep long short-term memory recurrent neural networks. IEEE Trans Industrial Inf 15(2):987–997. https://doi.org/10.1109/tii.2018.2866549

    Article  MATH  Google Scholar 

  8. Zhao R, Yan R, Chen Z, Mao K, Wang P, Gao RX (2019) Deep learning and its applications to machine health monitoring. Mech Syst Signal Process 115:213–237. https://doi.org/10.1016/j.ymssp.2018.05.050

    Article  MATH  Google Scholar 

  9. Ren Z, Lin T, Feng K, Zhu Y, Liu Z, Yan K (2023) A systematic review on imbalanced learning methods in intelligent fault diagnosis. IEEE Trans Instrument Measure 72:1–35. https://doi.org/10.1109/tim.2023.3246470

    Article  MATH  Google Scholar 

  10. Xu Y, Cheng X, Ke W, Zhu Q-X, He Y-L, Zhang Y (2022). SMOTE-Based Fault Diagnosis Method for Unbalanced Samples. https://doi.org/10.1109/ddcls55054.2022.9858365

  11. Wei J, Huang H, Yao L, Hu Y, Fan Q, Huang D (2021) New imbalanced bearing fault diagnosis method based on sample-characteristic oversampling technique (scote) and multi-class ls-svm. Appl Soft Comput 101. https://doi.org/10.1016/j.asoc.2020.107043

  12. Zhang W, Li X, Jia X-D, Ma H, Luo Z, Li X (2020) Machinery fault diagnosis with imbalanced data using deep generative adversarial networks. Measurement 152. https://doi.org/10.1016/j.measurement.2019.107377

  13. Cui J, Zong L, Xie J, Tang M (2023) A novel multi-module integrated intrusion detection system for high-dimensional imbalanced data. Appl Intell 53(1):272–288. https://doi.org/10.1007/s10489-022-03361-2

    Article  MATH  Google Scholar 

  14. Gao X, Deng F, Yue X (2020) Data augmentation in fault diagnosis based on the wasserstein generative adversarial network with gradient penalty. Neurocomputing 396:487–494. https://doi.org/10.1016/j.neucom.2018.10.109

    Article  MATH  Google Scholar 

  15. Zheng M, Li T, Zhu R, Tang Y, Tang M, Lin L, Ma Z (2020) Conditional wasserstein generative adversarial network-gradient penalty-based approach to alleviating imbalanced data classification. Inf Sci 512:1009–1023. https://doi.org/10.1016/j.ins.2019.10.014

    Article  MATH  Google Scholar 

  16. Shao S, Wang P, Yan R (2019) Generative adversarial networks for data augmentation in machine fault diagnosis. Comput Industry 106:85–93. https://doi.org/10.1016/j.compind.2019.01.001

    Article  MATH  Google Scholar 

  17. Li Z, Zheng T, Wang Y, Cao Z, Guo Z, Fu H (2021) A novel method for imbalanced fault diagnosis of rotating machinery based on generative adversarial networks. IEEE Trans Instrument Measure 70:1–17. https://doi.org/10.1109/tim.2020.3009343

    Article  MATH  Google Scholar 

  18. Lin TY, Goyal P, Girshick R, He K, Dollár P (2017) Focal loss for dense object detection. In: 2017 IEEE International conference on computer vision (ICCV), pp 2999–3007. https://doi.org/10.1109/ICCV.2017.324

  19. Jia F, Lei Y, Lu N, Xing S (2018) Deep normalized convolutional neural network for imbalanced fault classification of machinery and its understanding via visualization. Mech Syst Signal Process 110:349–367. https://doi.org/10.1016/j.ymssp.2018.03.025

    Article  MATH  Google Scholar 

  20. Cui Y, Jia M, Lin TY, Song Y, Belongie S (2019) Class-balanced loss based on effective number of samples. In: 2019 IEEE/CVF Conference on computer vision and pattern recognition (CVPR), pp 9260–9269. https://doi.org/10.1109/CVPR.2019.00949

  21. Fernando KRM, Tsokos CP (2022) Dynamically weighted balanced loss: Class imbalanced learning and confidence calibration of deep neural networks. IEEE Trans Neural Netw Learn Syst 33(7):2940–2951. https://doi.org/10.1109/TNNLS.2020.3047335

    Article  MATH  Google Scholar 

  22. Yan S, Zhong X, Shao H, Ming Y, Liu C, Liu B (2023) Digital twin-assisted imbalanced fault diagnosis framework using subdomain adaptive mechanism and margin-aware regularization. Reliability Eng Syst Safety 239. https://doi.org/10.1016/j.ress.2023.109522

  23. Yan S, Shao H, Xiao Y, Zhou J, Xu Y, Wan J (2022) Semi-supervised fault diagnosis of machinery using lps-dgat under speed fluctuation and extremely low labeled rates. Adv Eng Inf 53:101648. https://doi.org/10.1016/j.aei.2022.101648

  24. Pan H, Xu H, Zheng J, Shao H, Tong J (2024) A semi-supervised matrixized graph embedding machine for roller bearing fault diagnosis under few-labeled samples. IEEE Trans Industrial Inf 20(1):854–863. https://doi.org/10.1109/TII.2023.3265525

    Article  MATH  Google Scholar 

  25. Kaya M, Bilge HS (2019) Deep metric learning: A survey. Symmetry 11(9). https://doi.org/10.3390/sym11091066

  26. Zhou J, Zhang X, Jiang H, Shao Z, Ma B, Zhou R (2024) Mc-wdwcnn: an interpretable multi-channel wide-kernel wavelet convolutional neural network for strong noise-robust fault diagnosis. Measure Sci Technol 35(9):096125. https://doi.org/10.1088/1361-6501/ad56b8

  27. Shao Z, Jiang H, Zhang X, Zhou J, Hu X (2024) Application of wavelet dynamic joint adaptive network guided by pseudo-label alignment mechanism in gearbox fault diagnosis. Measure Sci Technol 35(11):116111. https://doi.org/10.1088/1361-6501/ad67f6

    Article  MATH  Google Scholar 

  28. Jiang B, Zhang Z, Lin D, Tang J, Luo B (2019) Semi-supervised learning with graph learning-convolutional networks. In: 2019 IEEE/CVF Conference on computer vision and pattern recognition (CVPR), pp 11305–11312. https://doi.org/10.1109/CVPR.2019.01157

  29. Hamilton WL, Ying R, Leskovec J (2017) Inductive representation learning on large graphs. Curran Associates Inc. https://doi.org/10.5555/3294771.:3294869

  30. Li T, Zhou Z, Li S, Sun C, Yan R, Chen X (2022) The emerging graph neural networks for intelligent fault diagnostics and prognostics: A guideline and a benchmark study. Mech Syst Signal Process 168. https://doi.org/10.1016/j.ymssp.2021.108653

  31. Yang C, Liu J, Zhou K, Jiang X, Ge MF, Liu Y (2022) A node-level pathgraph-based bearing remaining useful life prediction method. IEEE Trans Instrument Measure 71:1–10. https://doi.org/10.1109/TIM.2022.3190526

    Article  MATH  Google Scholar 

  32. Rong Y, Huang W, Xu T, Huang J (2020) Dropedge: Towards deep graph convolutional networks on node classification. In: International conference on learning representations. https://doi.org/10.48550/arXiv.1907.10903

  33. Zhu Y, Yang Z, Wang L, Zhao S, Hu X, Tao D (2020) Hetero-center loss for cross-modality person re-identification. Neurocomputing 386:97–109. https://doi.org/10.1016/j.neucom.2019.12.100

    Article  MATH  Google Scholar 

  34. Shao S, McAleer S, Yan R, Baldi P (2019) Highly accurate machine fault diagnosis using deep transfer learning. IEEE Trans Industrial Inf 15(4):2446–2455. https://doi.org/10.1109/tii.2018.2864759

    Article  Google Scholar 

  35. Velikovi P, Cucurull G, Casanova A, Romero A, Liò P, Bengio Y (2017) Graph attention networks, 39–41. https://doi.org/10.48550/arXiv.1710.10903

  36. Xu K, Hu W, Leskovec J, Jegelka S (2018) How powerful are graph neural networks? https://doi.org/10.48550/arXiv.1810.00826

  37. Wu F, Zhang T, Souza Jr. au2 AH, Fifty C, Yu T, Weinberger KQ (2019) Simplifying graph convolutional networks. https://doi.org/10.48550/arXiv.1902.07153

  38. Kishan KC, Li R, Cui F, Haake AR (2022) Predicting biomedical interactions with higher-order graph convolutional networks. IEEE/ACM Trans Computat Biol Bioinf 19(2):676–687. https://doi.org/10.1109/TCBB.2021.3059415

    Article  MATH  Google Scholar 

  39. Tang S, Li B, Yu H (2019) Chebnet: Efficient and stable constructions of deep neural networks with rectified power units using chebyshev approximations. https://doi.org/10.48550/arXiv.1911.05467

  40. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 770–778. https://doi.org/10.1109/CVPR.2016.90

  41. Li T, Zhao Z, Sun C, Yan R, Chen X (2021) Domain adversarial graph convolutional network for fault diagnosis under variable working conditions. IEEE Trans Instrument Measure 70:1–10. https://doi.org/10.1109/tim.2021.3075016

    Article  MATH  Google Scholar 

  42. Tolstikhin I, Houlsby N, Kolesnikov A, Beyer L, Dosovitskiy A (2021) Mlp-mixer: An all-mlp architecture for vision. https://doi.org/10.48550/arXiv.2105.01601

  43. Maaten L, Hinton G (2008) Visualizing data using t-sne. J Mach Learn Res 9(86):2579–2605

    MATH  Google Scholar 

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Acknowledgements

This work was supported in part by the Major Science and Technology Programs in Xinjiang Uygur Autonomous Region (No.2022A02010-3).

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Jianyu Zhou: Methodology, Supervision, Conceptualization, Software, Investigation, Writing original draft. Xiangfeng Zhang: Methodology, Supervision, Formal analysis, Funding acquisition. Hong Jiang: Formal analysis, Writing-review and editing, Funding acquisition. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Xiangfeng Zhang.

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Zhou, J., Zhang, X. & Jiang, H. Multi-scale GraphSAGE with class center balancing loss for rolling bearing fault diagnosis under extremely class imbalance. Appl Intell 55, 51 (2025). https://doi.org/10.1007/s10489-024-05960-7

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