Skip to main content
SpringerLink
Log in

Train bearing fault diagnosis based on multi-sensor data fusion and dual-scale residual network

  • Original Paper
  • Published:
Nonlinear Dynamics Aims and scope Submit manuscript

Abstract

As one of the vital components of trains, the condition of train bearings is closely related to the safe operation of trains. Traditional bearing fault diagnosis methods based on single sensors are incapable of extracting feature information fully, resulting in low fault diagnostic accuracy. To solve the above problem, a fault diagnosis method for train bearings based on multi-sensor data fusion and dual-scale residual network (MSDF-DSRNet) is proposed in this paper. Firstly, a multi-sensor data fusion method is designed to extract fault feature information comprehensively. The low-dimensional features embedded in the high-dimensional nonlinear space of multi-sensor data are extracted effectively and fused into a three-dimensional pixel matrix. Secondly, a novel intelligent diagnosis method is proposed based on the dual-scale residual network. Both in-depth and shallow features are learned on two scales, and the fault-related information in different spatial dimensions is captured, which improves the extraction ability of fusion features and effectively reduces time loss. Finally, the feasibility and effectiveness of the proposed method are verified by three experiments. The accuracy of the proposed method in the train traction motor bearing dataset reaches 99.70%, 99.75%, 99.85% and 99.85%, respectively. The results show that MSDF-DSRNet performs better in comprehensive fault diagnosis than other methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22

Similar content being viewed by others

Data availability

The datasets generated during and analyzed during the current study are available from the corresponding authors on reasonable request.

References

  1. He, D., Liu, C., Jin, Z., Ma, R., Chen, Y., Shan, S.: Fault diagnosis of flywheel bearing based on parameter optimization variational mode decomposition energy entropy and deep learning. Energy 239, 122108 (2022)

    Article  Google Scholar 

  2. Ren, H., Wang, J., Dai, J., Zhu, Z., Liu, J.: Dynamic balanced domain-adversarial networks for cross-domain fault diagnosis of train bearings. IEEE Trans. Instrum. Meas. 71, 1 (2022)

    Google Scholar 

  3. Wei, Z., He, D., Jin, Z., Liu, B., Shan, S., Chen, Y., Miao, J.: Density-based affinity propagation tensor clustering for intelligent fault diagnosis of train bogie bearing. IEEE Trans. Intell. Transp. Syst. (2023). https://doi.org/10.1109/TITS.2023.3253087

    Article  Google Scholar 

  4. Dang, W., Gao, Z., Sun, X., Li, R., Cai, Q., Grebogi, C.: Multilayer brain network combined with deep convolutional neural network for detecting major depressive disorder. Nonlinear Dyn. 102, 667 (2020)

    Article  Google Scholar 

  5. Ding, A., Qin, Y., Wang, B., Jia, L., Cheng, X.: Lightweight multiscale convolutional networks with adaptive pruning for intelligent fault diagnosis of train bogie bearings in edge computing scenarios. IEEE Trans. Instrum. Meas. 72, 1–13 (2023). https://doi.org/10.1109/TIM.2022.3231325

    Article  Google Scholar 

  6. Xin, G., Li, Z., Jia, L., Zhong, Q., Dong, H., Hamzaoui, N., Antoni, J.: Fault diagnosis of wheelset bearings in high-speed trains using logarithmic short-time fourier transform and modified self-calibrated residual network. IEEE Trans. Industr. Inf. 18, 7285 (2022)

    Article  Google Scholar 

  7. Jin, Z., He, D., Wei, Z.: Intelligent fault diagnosis of train axle box bearing based on parameter optimization VMD and improved DBN. Eng. Appl. Artif. Intell. 110, 104713 (2022)

    Article  Google Scholar 

  8. Xia, M., Li, T., Xu, L., Liu, L., Clarence, W.D.S.: Fault diagnosis for rotating machinery using multiple sensors and convolutional neural networks. IEEE/ASME Trans. Mechatron. 23, 101 (2018)

    Article  Google Scholar 

  9. Liu, J., Hu, Y., Wang, Y., Wu, B., Fan, J., Hu, Z.: An integrated multi-sensor fusion-based deep feature learning approach for rotating machinery diagnosis. Meas. Sci. Technol. 29, 55103 (2018)

    Article  Google Scholar 

  10. Lao, Z., He, D., Wei, Z., Shang, H., Jin, Z., Miao, J., Ren, C.: Intelligent fault diagnosis for rail transit switch machine based on adaptive feature selection and improved LightGBM. Eng. Fail. Anal. 148, 107219 (2023)

    Article  Google Scholar 

  11. Hoang, D., Kang, H.: A motor current signal-based bearing fault diagnosis using deep learning and information fusion. IEEE Trans. Instrum. Meas. 69, 3325 (2020)

    Article  Google Scholar 

  12. Zheng, J., Chen, Y., Pan, H., Tong, J.: Composite multi-scale phase reverse permutation entropy and its application to fault diagnosis of rolling bearing. Nonlinear Dyn. (2022). https://doi.org/10.1007/s11071-022-07847-z

    Article  Google Scholar 

  13. Wang, Z., Chen, H., Yao, L., Chen, X., Qi, X., Zhang, J.: An effective multi-channel fault diagnosis approach for rotating machinery based on multivariate generalized refined composite multi-scale sample entropy. Nonlinear Dyn. 106, 2107 (2021)

    Article  Google Scholar 

  14. Zhu, J., Hu, T., Jiang, B., Yang, X.: Intelligent bearing fault diagnosis using PCA-DBN framework. Neural Comput. Appl. 32, 10773 (2020)

    Article  Google Scholar 

  15. Ou, D., Xue, R., Cui, K.: A data-driven fault diagnosis method for railway turnouts. Transp. Res. Rec. 2673, 448 (2019)

    Article  Google Scholar 

  16. Kong, X., Yang, Z., Luo, J., Li, H., Yang, X.: Extraction of reduced fault subspace based on KDICA and its application in fault diagnosis. IEEE Trans. Instrum. Meas. 71, 1 (2022)

    Google Scholar 

  17. He, X., Niyogi, P. Locality preserving projections. In: Proceedings of the 16th International Conference on Neural Information Processing Systems, pp. 153. Whistler, British Columbia, Canada (2003).

  18. Kaplan, K., Kaya, Y., Kuncan, M., Minaz, M.R., Ertunç, H.M.: An improved feature extraction method using texture analysis with LBP for bearing fault diagnosis. Appl Soft Comput 87, 106019 (2020)

    Article  Google Scholar 

  19. Liu, X., Li, Y., Sun, M., Sun, Z., Zang, J.: A model of binaural auditory nerve oscillator network for bearing fault diagnosis by integrating two-channel vibration signals. Nonlinear Dyn. 111, 4779 (2023)

    Article  Google Scholar 

  20. Wei, H., Zhang, Q., Shang, M., Gu, Y.: Extreme learning Machine-based classifier for fault diagnosis of rotating machinery using a residual network and continuous wavelet transform. Measurement 183, 109864 (2021)

    Article  Google Scholar 

  21. Ai, Y., Guan, J., Fei, C., Tian, J., Zhang, F.: Fusion information entropy method of rolling bearing fault diagnosis based on n-dimensional characteristic parameter distance. Mech. Syst. Signal Process. 88, 123 (2017)

    Article  Google Scholar 

  22. Wang, J., Wang, D., Wang, S., Li, W., Song, K.: Fault diagnosis of bearings based on multi-sensor information fusion and 2D convolutional neural network. IEEE Access. 9, 23717 (2021)

    Article  Google Scholar 

  23. Xu, X., Tao, Z., Ming, W., An, Q., Chen, M.: Intelligent monitoring and diagnostics using a novel integrated model based on deep learning and multi-sensor feature fusion. Measurement 165, 108086 (2020)

    Article  Google Scholar 

  24. Xie, T., Huang, X., Choi, S.: Intelligent mechanical fault diagnosis using multisensor fusion and convolution neural network. IEEE Trans. Industr. Inf. 18, 3213 (2022)

    Article  Google Scholar 

  25. Luo, Z., Tan, H., Dong, X., Zhu, G., Li, J.: A fault diagnosis method for rotating machinery with variable speed based on multi-feature fusion and improved ShuffleNet V2. Meas. Sci. Technol. 34, 35110 (2023)

    Article  Google Scholar 

  26. Chao, Q., Gao, H., Tao, J., Liu, C., Wang, Y., Zhou, J.: Fault diagnosis of axial piston pumps with multi-sensor data and convolutional neural network. Front. Mech. Eng. 17, 36 (2022)

    Article  Google Scholar 

  27. Liu, C., Tong, J., Zheng, J., Pan, H., Bao, J.: Rolling bearing fault diagnosis method based on multi-sensor two-stage fusion. Meas. Sci. Technol. 33, 125105 (2022)

    Article  Google Scholar 

  28. Zhu, X., Zhao, J., Hou, D., Han, Z., Glowacz, A.: An SDP characteristic information fusion-based CNN vibration fault diagnosis method. Shock. Vib. 2019, 3926963 (2019)

    Google Scholar 

  29. Ma, X., Wang, P., Zhang, B., Sun, M., Jove, E.: A multirate sensor information fusion strategy for multitask fault diagnosis based on convolutional neural network. Journal of Sensors. 2021, 9952450 (2021)

    Article  Google Scholar 

  30. Bai, T., Yang, J., Yao, D., Wang, Y., Shi, H.: Information fusion of infrared images and vibration signals for coupling fault diagnosis of rotating machinery. Shock. Vib. 2021, 6622041 (2021)

    Google Scholar 

  31. Man, J., Dong, H., Jia, L., Qin, Y., Zhang, J.: An adaptive multisensor fault diagnosis method for high-speed train bogie. IEEE Trans. Intell. Transp. Syst. (2023). https://doi.org/10.1109/TITS.2023.3251341

    Article  Google Scholar 

  32. Zhang, K., Gao, T., Shi, H.: Bearing fault diagnosis method based on multi-source heterogeneous information fusion. Meas. Sci. Technol. 33, 75901 (2022)

    Article  Google Scholar 

  33. Wang, H., Du, W.: Multi-source information deep fusion for rolling bearing fault diagnosis based on deep residual convolution neural network. Proc. Inst. Mech. Eng. Part C J. Mech. Eng. 236, 7576 (2022)

    Article  Google Scholar 

  34. Sun, J., Wen, J., Yuan, C., Liu, Z., Xiao, Q.: Bearing fault diagnosis based on multiple transformation domain fusion and improved residual dense networks. IEEE Sens. J. 22, 1541 (2022)

    Article  Google Scholar 

  35. Peng, B., Xia, H., Lv, X., Annor-Nyarko, M., Zhu, S., Liu, Y.: An intelligent fault diagnosis method for rotating machinery based on data fusion and deep residual neural network. Appl. Intell. 52, 3051 (2022)

    Article  Google Scholar 

  36. Wang, Z., Wen, C., Dong, Y.: A method for rolling bearing fault diagnosis based on GSC-MDRNN with multi-dimensional input. Meas. Sci. Technol. 34, 55901 (2023)

    Article  Google Scholar 

  37. Chen, W., Sun, K., Li, X., Xiao, Y., Xiang, J., Mao, H.: Adaptive multi-channel residual shrinkage networks for the diagnosis of multi-fault gearbox. Appl. Sci. 13(3), 1714 (2023)

    Article  Google Scholar 

  38. Huang, H., Tang, B., Luo, J., Pu, H., Zhang, K.: Residual gated dynamic sparse network for gearbox fault diagnosis using multisensor data. IEEE Trans. Industr. Inf. 18, 2264 (2022)

    Article  Google Scholar 

  39. He, K., Zhang, X., Ren, S., Sun, J. Deep Residual Learning for Image Recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770. Las Vegas, NV, USA (2016).

  40. Hou, Q., Zhang, L., Cheng, M., Feng, J. Strip Pooling: Rethinking Spatial Pooling for Scene Parsing. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 4002–4011. Seattle, WA, USA (2020).

  41. Bai, H., Zhan, X., Yan, H., Wen, L., Jia, X.: Combination of optimized variational mode decomposition and deep transfer learning: a better fault diagnosis approach for diesel engines. Electronics 11(13), 1969 (2022)

    Article  Google Scholar 

  42. Akhenia, P., Bhavsar, K., Panchal, J., Vakharia, V.: Fault severity classification of ball bearing using SinGAN and deep convolutional neural network. Proc. Inst. Mech. Eng. Part C J. Mech. Eng. Sci. 236, 3864 (2021)

    Article  Google Scholar 

  43. Ning, S., Wang, Y., Cai, W., Zhang, Z., Wu, Y., Ren, Y.: Research on intelligent fault diagnosis of rolling bearing based on improved shufflenetV2-LSTM. J. Sensors. 2022, 8522206 (2022)

    Article  Google Scholar 

  44. Wen, L., Li, X., Gao, L.: A transfer convolutional neural network for fault diagnosis based on ResNet-50. Neural Comput. Appl. 32, 6111 (2020)

    Article  Google Scholar 

  45. Peng, D., Wang, H., Liu, Z., Zhang, W., Zuo, M., Chen, J.: Multibranch and multiscale CNN for fault diagnosis of wheelset bearings under strong noise and variable load condition. IEEE Trans. Ind. Inf. 16, 4949 (2020)

    Article  Google Scholar 

  46. Shao, S., McAleer, S., Yan, R., Baldi, P.: Highly accurate machine fault diagnosis using deep transfer learning. IEEE Trans. Ind. Inf. 15, 2446 (2019)

    Article  Google Scholar 

Download references

Funding

The research was supported by the National Natural Science Foundation of China [Grant No. U22A2053], the Major Project of Science and Technology of Guangxi Province of China [Grant No. Guike AA20302010], the Interdisciplinary Scientific Research Foundation of Guangxi University [Grant No. 2022JCA003], the Guangxi Manufacturing Systems and Advanced Manufacturing Technology Key Laboratory Director Fund [Grant No. 21-050-44-S015] and the Innovation Project of Guangxi Graduate Education [Grant No. YCSW2023086].

Author information

Authors and Affiliations

  1. Guangxi Key Laboratory of Manufacturing System and Advanced Manufacturing Technology, School of Mechanical Engineering, Guangxi University, Nanning, 530004, China

    Deqiang He, Zhenpeng Lao, Zhenzhen Jin, Changfu He & Jian Miao

  2. CRRC Zhuzhou Institute Co., Ltd, Zhuzhou, 412001, China

    Sheng Shan

Authors
  1. Deqiang He
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhenpeng Lao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhenzhen Jin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Changfu He
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sheng Shan
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jian Miao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Deqiang He.

Ethics declarations

Conflicts of interest

The authors declare no potential conflicts of interest concerning the research, authorship and/or publication of this paper.

Ethics approval

The experimental protocol was established according to the ethical guidelines of the Helsinki Declaration and was approved by the Human Ethics Committee of Guangxi University.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

He, D., Lao, Z., Jin, Z. et al. Train bearing fault diagnosis based on multi-sensor data fusion and dual-scale residual network. Nonlinear Dyn 111, 14901–14924 (2023). https://doi.org/10.1007/s11071-023-08638-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11071-023-08638-w

Keywords

Search

Navigation