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A novel deep learning approach for intelligent bearing fault diagnosis under extremely small samples

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Abstract

Rotor bearing health is crucial for ensuring the operational stability of rotating equipment. Deep learning-based fault diagnosis methods have achieved widespread success due to their superior fault identification capability. However, conventional deep learning methods that rely on large quantities of data are not feasible for most important mechanical equipment since obtaining fault data is difficult. To address this problem, we propose channel attention siamese networks (CASN) with metric learning for intelligent bearing fault diagnosis with extremely small samples. First, in the feature learning phase, pairs of sample inputs are constructed, and feature extraction is performed by a shared encoder. Then, in the disparity learning phase, the differences between features of sample pairs are mapped as metric distances. Based on the metric distance between the unlabeled and labeled data, the fault type of the unlabeled data can be predicted in the test phase. The experimental results show that CASN achieves over 97% accuracy when the sample size is extremely small. In addition, even under the conditions of noise interference and signal transmission distortion, our model still has reliable diagnostic ability.

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Data Availability

The CWRU dataset used in this work can be accessible through https://engineering.case.edu/bearingdatacenter.

Abbreviations

CASN:

Channel attention siamese networks

CNN:

Convolutional neural networks

Conv:

Convolutional layer

FC:

Fully connected layer

SE:

Squeeze-and-excitation

SNN:

Siamese neural networks

References

  1. Zhao Y, Chen Y (2022) Extreme learning machine based transfer learning for aero engine fault diagnosis. Aerosp Sci Technol 121:107311. https://doi.org/10.1016/j.ast.2021.107311

  2. Wang X, Zhang H, Du Z (2023) Multi-scale noise reduction attention network for aero-engine bearing fault diagnosis. IEEE Trans Instrum Meas 72:1–10. https://doi.org/10.1109/TIM.2023.3268459

    Article  Google Scholar 

  3. Zio E (2022) Prognostics and Health Management (PHM): Where are we and where do we (need to) go in theory and practice. Reliab Eng Syst Saf 218:108119

    Article  Google Scholar 

  4. Zhou F, Yang S, Fujita H, Chen D, Wen C (2020) Deep learning fault diagnosis method based on global optimization GAN for unbalanced data. Knowl-Based Syst 187:104837

    Article  Google Scholar 

  5. Xu Z, Saleh JH (2021) Machine learning for reliability engineering and safety applications: Review of current status and future opportunities. Reliab Eng Syst Saf 211:107530

    Article  Google Scholar 

  6. Zhou F, Liu S, Fujita H, Hu X, Zhang Y, Wang B, Wang K (2024) Fault diagnosis based on federated learning driven by dynamic expansion for model layers of imbalanced client. Expert Syst Appl 238:121982

    Article  Google Scholar 

  7. Serradilla O, Zugasti E, Rodriguez J, Zurutuza U (2022) Deep learning models for predictive maintenance: a survey, comparison, challenges and prospects. Appl Intell 52(10):10934–10964

    Article  Google Scholar 

  8. Darvishi H, Ciuonzo D, Rossi PS (2023) A machine-learning architecture for sensor fault detection, isolation, and accommodation in digital twins. IEEE Sens J 23(3):2522–2538. https://doi.org/10.1109/JSEN.2022.3227713

    Article  Google Scholar 

  9. Chen Z, He G, Li J, Liao Y, Gryllias K, Li W (2020) Domain adversarial transfer network for cross-domain fault diagnosis of rotary machinery. IEEE Trans Instrum Meas 69(11):8702–8712

    Article  Google Scholar 

  10. Khorram A, Khalooei M, Rezghi M (2021) End-to-end CNN+ LSTM deep learning approach for bearing fault diagnosis. Appl Intell 51:736–751

    Article  Google Scholar 

  11. Darvishi H, Ciuonzo D, Salvo Rossi P (2023) Deep Recurrent Graph Convolutional Architecture for Sensor Fault Detection. Isolation, and Accommodation in Digital Twins, IEEE Sensors Journal 23(23):29877–29891

    Google Scholar 

  12. Liu S, Gao F, Sun X (2022) Continual learning classification method and its application to equipment fault diagnosis. Appl Intell 52(1):858–874

    Article  Google Scholar 

  13. Darvishi H, Ciuonzo D, Eide ER, Rossi PS (2021) Sensor-fault detection. isolation and accommodation for digital twins via modular data-driven architecture. IEEE Sens J 21(4):4827–4838. https://doi.org/10.1109/JSEN.2020.3029459

  14. Lu N, Hu H, Yin T, Lei Y, Wang S (2021) Transfer relation network for fault diagnosis of rotating machinery with small data. IEEE Transactions on Cybernetics 52(11):11927–11941

    Article  Google Scholar 

  15. Li X, Li X, Ma H (2020) Deep representation clustering-based fault diagnosis method with unsupervised data applied to rotating machinery. Mech Syst Signal Process 143:106825

    Article  Google Scholar 

  16. Wang D, Zhang M, Xu Y, Lu W, Yang J, Zhang T (2021) Metric-based meta-learning model for few-shot fault diagnosis under multiple limited data conditions. Mech Syst Signal Process 155:107510

    Article  Google Scholar 

  17. Yu K, Lin TR, Ma H, Li X, Li X (2021) A multi-stage semi-supervised learning approach for intelligent fault diagnosis of rolling bearing using data augmentation and metric learning. Mech Syst Signal Process 146:107043

    Article  Google Scholar 

  18. Zhang X, Su Z, Hu X, Han Y, Wang S (2022) Semisupervised momentum prototype network for gearbox fault diagnosis under limited labeled samples. IEEE Trans Industr Inf 18(9):6203–6213

    Article  Google Scholar 

  19. Zhang A, Li S, Cui Y, Yang W, Dong R, Hu J (2019) Limited data rolling bearing fault diagnosis with few-shot learning. Ieee Access 7:110895–110904

    Article  Google Scholar 

  20. Dixit S, Verma NK (2020) Intelligent condition-based monitoring of rotary machines with few samples. IEEE Sens J 20(23):14337–14346

    Article  Google Scholar 

  21. Saufi SR, Ahmad ZAB, Leong MS, Lim MH (2020) Gearbox fault diagnosis using a deep learning model with limited data sample. IEEE Trans Industr Inf 16(10):6263–6271

    Article  Google Scholar 

  22. Li C, Li S, Zhang A, He Q, Liao Z, Hu J (2021) Meta-learning for few-shot bearing fault diagnosis under complex working conditions. Neurocomputing 439:197–211

    Article  Google Scholar 

  23. Yu C, Ning Y, Qin Y, Su W, Zhao X (2021) Multi-label fault diagnosis of rolling bearing based on meta-learning. Neural Comput Appl 33:5393–5407

    Article  Google Scholar 

  24. Zhang K, Tang B, Deng L, Tan Q, Yu H (2021) A fault diagnosis method for wind turbines gearbox based on adaptive loss weighted meta-ResNet under noisy labels. Mech Syst Signal Process 161:107963

    Article  Google Scholar 

  25. Zhang S, Ye F, Wang B, Habetler TG (2021) Few-shot bearing fault diagnosis based on model-agnostic meta-learning. IEEE Trans Ind Appl 57(5):4754–4764

    Article  Google Scholar 

  26. Hospedales T, Antoniou A, Micaelli P, Storkey A (2021) Meta-learning in neural networks: A survey. IEEE Trans Pattern Anal Mach Intell 44(9):5149–5169

    Google Scholar 

  27. Hu J, Shen L, Albanie S, Sun G, Wu E (2017) Squeeze-and-Excitation Networks. IEEE Trans Pattern Anal Mach Intell 42:2011–2023

    Article  Google Scholar 

  28. Smith WA, Randall RB (2015) Rolling element bearing diagnostics using the CaseWestern Reserve University data: a benchmark study. Mech Syst Signal Process 64–65:100–131. https://doi.org/10.1016/j.ymssp.2015.04.021

    Article  Google Scholar 

  29. Xu M, Yoon S, Fuentes A, Park DS (2023) A comprehensive survey of image augmentation techniques for deep learning. Pattern Recognit 137:109347. https://doi.org/10.1016/j.patcog.2023.109347

  30. Zhang W, Li C, Peng G, Chen Y, Zhang Z (2018) A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load. Mech Syst Signal Process 100:439–453

    Article  Google Scholar 

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Acknowledgements

This work was supported by the National Science and Technology Major Project under Grant 2019-I-0019-0018.

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Authors and Affiliations

  1. School of Control Science and Engineering, Dalian University of Technology, DalianLiaoning, 116024, China

    Peixuan Ding, Pan Qin & Xi-Ming Sun

  2. School of Artificial Intelligence, Dalian University of Technology, DalianLiaoning, 116024, China

    Yi Xu

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  1. Peixuan Ding
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  2. Yi Xu
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  3. Pan Qin
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  4. Xi-Ming Sun
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Contributions

All the authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Peixuan Ding. The first draft of the manuscript was written by Peixuan Ding, and all the authors commented on previous versions of the manuscript. All the authors read and approved the final manuscript.

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Correspondence to Yi Xu.

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Ding, P., Xu, Y., Qin, P. et al. A novel deep learning approach for intelligent bearing fault diagnosis under extremely small samples. Appl Intell (2024). https://doi.org/10.1007/s10489-024-05429-7

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