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Multi-subspace self-attention siamese networks for fault diagnosis with limited data

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Abstract

It has always been an important issue to diagnose mechanical equipment faults with limited training data. Specifically for the problem of bearing fault diagnosis, a multi-subspace self-attention siamese network (MSSASN) is designed for fault diagnosis with limited training data. In MSSASN, multi-subspace self-attention block is developed to assign higher weights to the fault-related information during learning. Particularly, input features are divided into multiple sub-paths in the channel dimension, and the spatial attention features are calculated separately on sub-path and then merged. In this way, the cross-channel information can be effectively learned, while multi-scale feature learning is carried out. Finally, contrastive learning is carried out on the fault features of different samples using siamese networks to deal with the problem of limited training samples. The proposed method is verified by the vibration dataset collected from the three-phase asynchronous motor experiment platform in Zhejiang University of Technology. The results show that the proposed method can identify rolling bearing faults more accurately with limited training data.

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Data Availability

Some of the data and materials supporting the results of this study can be obtained from the corresponding authors upon reasonable request.

References

  1. Lei, Y., Yang, B., Jiang, X., Jia, F., Li, N., Nandi, A.K.: Applications of machine learning to machine fault diagnosis: a review and roadmap. Mech. Syst. Signal Process. 138, 106587 (2020)

    Article  Google Scholar 

  2. Lin, A., Cheng, J., Rutkowski, L., Wen, S., Luo, M., Cao, J.: Asynchronous fault detection for memristive neural networks with dwell-time-based communication protocol. IEEE Trans. Neural Netw. Learn. Syst. 34(11), 9004–9015 (2023)

    Article  MathSciNet  PubMed  Google Scholar 

  3. Cheng, J., Lin, A., Cao, J., Qiu, J., Qi, W.: Protocol-based fault detection for discrete-time memristive neural networks with quantization effect. Inf. Sci. 615, 118–135 (2022)

    Article  Google Scholar 

  4. Chen, Y., Zhang, D., Zhang, H., Wang, Q.-G.: Dual-path mixed-domain residual threshold networks for bearing fault diagnosis. IEEE Trans. Ind. Electron. 69(12), 13462–13472 (2022)

    Article  Google Scholar 

  5. Marticorena, M., Peyrano, O.G.: Rolling bearing condition monitoring technique based on cage rotation analysis and acoustic emission. J. Dyn. Monit. Diagn. 1(2), 57–65 (2022)

    Google Scholar 

  6. Han, S., Feng, Z.: Deep residual joint transfer strategy for cross-condition fault diagnosis of rolling bearings. J. Dyn. Monit. Diagn. 2(1), 51–60 (2023)

    Google Scholar 

  7. Hashempour, Z., Agahi, H., Mahmoodzadeh, A.: A novel method for fault diagnosis in rolling bearings based on bispectrum signals and combined feature extraction algorithms. SIViP 16, 1043–1051 (2022)

    Article  Google Scholar 

  8. Xu, Y., Tang, X., Feng, G., Wang, D., Ashworth, C., Gu, F., Ball, A.: Orthogonal on-rotor sensing vibrations for condition monitoring of rotating machines. J. Dyn. Monit. Diagn. 1(1), 29–36 (2022)

    Google Scholar 

  9. Zhang, X., Kong, J., Zhao, Y., Qian, W., Xu, X.: A deep-learning model with improved capsule networks and LSTM filters for bearing fault diagnosis. SIViP 17(4), 1325–1333 (2023)

    Article  Google Scholar 

  10. Zhang, Z., Zhou, F., Karimi, H.R., Fujita, H., Hu, X., Wen, C., Wang, T.: Attention gate guided multiscale recursive fusion strategy for deep neural network-based fault diagnosis. Eng. Appl. Artif. Intell. 126, 107052 (2023)

    Article  Google Scholar 

  11. Tian, J., Han, D., Karimi, H.R., Zhang, Y., Shi, P.: Deep learning-based open set multi-source domain adaptation with complementary transferability metric for mechanical fault diagnosis. Neural Netw. 162, 69–82 (2023)

    Article  PubMed  Google Scholar 

  12. Chen, Y., Zhang, D., Zhu, K., Yan, R.: An adaptive activation transfer learning approach for fault diagnosis. IEEE/ASME Trans. Mechatron. 28(5), 2645–2656 (2023)

    Article  Google Scholar 

  13. Yang, D., Karimi, H.R., Sun, K.: Residual wide-kernel deep convolutional auto-encoder for intelligent rotating machinery fault diagnosis with limited samples. Neural Netw. 141, 133–144 (2021)

    Article  PubMed  Google Scholar 

  14. Chen, Y., Zhang, D., Yan, R.: Domain adaptation networks with parameter-free adaptively rectified linear units for fault diagnosis under variable operating conditions. IEEE Trans. Neural Netw. Learn. Syst. (2023). https://doi.org/10.1109/TNNLS.2023.3298648

    Article  PubMed  Google Scholar 

  15. Zhang, Y., Ji, J., Ren, Z., Ni, Q., Gu, F., Feng, K., Yu, K., Ge, J., Lei, Z., Liu, Z.: Digital twin-driven partial domain adaptation network for intelligent fault diagnosis of rolling bearing. Reliab. Eng. Syst. Saf. 234, 109186 (2023)

    Article  Google Scholar 

  16. Wang, H., Liu, Z., Peng, D., Cheng, Z.: Attention-guided joint learning CNN with noise robustness for bearing fault diagnosis and vibration signal denoising. ISA Trans. 128, 470–484 (2022)

    Article  PubMed  Google Scholar 

  17. Li, X., Wan, S., Liu, S., Zhang, Y., Hong, J., Wang, D.: Bearing fault diagnosis method based on attention mechanism and multilayer fusion network. ISA Trans. 128, 550–564 (2022)

    Article  PubMed  Google Scholar 

  18. Zhou, F., Sun, T., Hu, X., Wang, T., Wen, C.: A sparse denoising deep neural network for improving fault diagnosis performance. SIViP 15(8), 1889–1898 (2021)

    Article  Google Scholar 

  19. Li, H., Wang, D.: Multilevel feature fusion of multi-domain vibration signals for bearing fault diagnosis. Signal Image Video Process. 1–10 (2023)

  20. Qiao, M., Yan, S., Tang, X., Xu, C.: Deep convolutional and LSTM recurrent neural networks for rolling bearing fault diagnosis under strong noises and variable loads. IEEE Access 8, 66257–66269 (2020)

    Article  Google Scholar 

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

    Article  Google Scholar 

  22. Luo, J., Huang, J., Li, H.: A case study of conditional deep convolutional generative adversarial networks in machine fault diagnosis. J. Intell. Manuf. 32, 407–425 (2021)

    Article  Google Scholar 

  23. Wang, R., Chen, Z., Zhang, S., Li, W.: Dual-attention generative adversarial networks for fault diagnosis under the class-imbalanced conditions. IEEE Sens. J. 22(2), 1474–1485 (2021)

    Article  ADS  CAS  Google Scholar 

  24. Zhao, M., Zhong, S., Fu, X., Tang, B., Pecht, M.: Deep residual shrinkage networks for fault diagnosis. IEEE Trans. Ind. Inf. 16(7), 4681–4690 (2019)

    Article  Google Scholar 

  25. Liu, H., Liu, F., Fan, X., Huang, D.: Polarized self-attention: Towards high-quality pixel-wise regression (2021). arXiv:2107.00782

  26. Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: Proceedings of the 27th international conference on machine learning (ICML-10), pp. 807–814 (2010)

  27. Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017)

  28. Ye, Z., Zhang, D., Deng, C., Yan, H., Feng, G.: Finite-time resilient sliding mode control of nonlinear UMV systems subject to dos attacks. Automatica 156, 111170 (2023)

    Article  MathSciNet  Google Scholar 

  29. Guo, X.-G., Liu, P.-M., Wu, Z.-G., Zhang, D., Ahn, C.K.: Hybrid event-triggered group consensus control for heterogeneous multi-agent systems with TVNUD faults and stochastic FDI attacks. IEEE Trans. Autom. Control (2023). https://doi.org/10.1109/TAC.2023.3254368

    Article  Google Scholar 

  30. Chen, Y., Zhang, D., Karimi, H.R., Deng, C., Yin, W.: A new deep learning framework based on blood pressure range constraint for continuous cuffless BP estimation. Neural Netw. 152, 181–190 (2022)

    Article  PubMed  Google Scholar 

  31. Panwar, M., Gautam, A., Biswas, D., Acharyya, A.: PP-Net: a deep learning framework for PPG-based blood pressure and heart rate estimation. IEEE Sens. J. 20(17), 10000–10011 (2020)

    Article  ADS  Google Scholar 

  32. Zhang, W., Peng, G., Li, C., Chen, Y., Zhang, Z.: A new deep learning model for fault diagnosis with good anti-noise and domain adaptation ability on raw vibration signals. Sensors 17(2), 425 (2017)

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  33. Song, X., Cong, Y., Song, Y., Chen, Y., Liang, P.: A bearing fault diagnosis model based on CNN with wide convolution kernels. J. Ambient Intell. Humaniz. Comput. 13(8), 4041–4056 (2022)

    Article  Google Scholar 

  34. Zhang, S., Wang, R., Si, Y., Wang, L.: An improved convolutional neural network for three-phase inverter fault diagnosis. IEEE Trans. Instrum. Meas. 71, 3510915 (2022)

    Google Scholar 

  35. Xu, Y., Yan, X., Sun, B., Zhai, J., Liu, Z.: Multireceptive field denoising residual convolutional networks for fault diagnosis. IEEE Trans. Ind. Electron. 69(11), 11686–11696 (2022)

    Article  Google Scholar 

  36. Zhang, L., Fan, Q., Lin, J., Zhang, Z., Yan, X., Li, C.: A nearly end-to-end deep learning approach to fault diagnosis of wind turbine gearboxes under nonstationary conditions. Eng. Appl. Artif. Intell. 119, 105735 (2023)

    Article  Google Scholar 

Download references

Funding

This work was partially supported by the National Natural Science Foundation of China under Grant Nos. 62322315 and 61873237, the Zhejiang Provincial Natural Science Foundation of China under Grant No. LR22F030003, the National Key R &D Funding under Grant No. 2018YFB1403702, the Key R &D Programs of Zhejiang Province under Grant No. 2023C01224 and Major Project of Science and Technology Innovation in Ningbo City under Grant No. 2019B1003.

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Authors and Affiliations

  1. School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai, 200240, China

    Xue Zhang

  2. Department of Automation, Zhejiang University of Technology, Hangzhou, 310023, Zhejiang Province, China

    Yongyi Chen, Hongjie Ni & Dan Zhang

  3. Center of Excellence in Intelligent Engineering Systems (CEIES), King Abdulaziz University, Jeddah, 310023, Saudi Arabia

    Mohammed Abdulaal

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  1. Xue Zhang
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  2. Yongyi Chen
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  3. Hongjie Ni
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  4. Dan Zhang
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  5. Mohammed Abdulaal
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Contributions

XZ and YC contributed to conceptualization, methodology, data processing, experimental validation and manuscript writing. HN, DZ and MA contributed to the modification of the manuscript.

Corresponding author

Correspondence to Dan Zhang.

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Zhang, X., Chen, Y., Ni, H. et al. Multi-subspace self-attention siamese networks for fault diagnosis with limited data. SIViP 18, 2465–2472 (2024). https://doi.org/10.1007/s11760-023-02922-3

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