Applied Intelligence

, Volume 49, Issue 11, pp 3923–3937 | Cite as

A multi-perspective architecture for high-speed train fault diagnosis based on variational mode decomposition and enhanced multi-scale structure

  • Yunpu WuEmail author
  • Weidong Jin
  • Junxiao Ren
  • Zhang Sun


The performance degradation and failure of high-speed train bogie would directly threaten the safe long-term operation of the vehicle. The fault diagnosis based on vibration signals is encountering difficulties as nonlinearity, high complexity, strong coupling, and high uncertainty. To address these challenges, this paper proposes a multi-perspective architecture for fault diagnosis, based on variational mode decomposition and enhanced multi-scale convolutional neural network. The proposed method provides multiple perspectives for the multi-channel and multi-component signal analysis, including perspectives from channel, component and time scale, with low input dimension and reduced model complexity. Signal features under different perspectives can be adaptively extracted. The effectiveness of the proposed method is validated on high-speed train fault data and rolling element bearings dataset. The experimental results show that the proposed scheme not only improves the accuracy of fault diagnosis but also has superior noise robustness which could be valuable for practical applications of complex systems, especially in dynamic environments.


Fault diagnosis High-speed train Variational mode decomposition Multi-scale structure Neural network 



The authors also thank the anonymous reviewers for his/her helpful remarks on our work.


  1. 1.
    Chen Z, Li W (2017) Multisensor feature fusion for bearing fault diagnosis using sparse autoencoder and deep belief network. IEEE Trans Instrum Meas 66(7):1693–1702. CrossRefGoogle Scholar
  2. 2.
    Chollet F (2017) Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 1251–1258Google Scholar
  3. 3.
    Cui Z, Chen W, Chen Y (2016) Multi-Scale Convolutional Neural Networks, for Time Series Classification. arXiv:1603.06995 [cs]
  4. 4.
    Dragomiretskiy K, Zosso D (2014) Variational Mode Decomposition. IEEE Trans Signal Process 62(3):531–544. CrossRefMathSciNetzbMATHGoogle Scholar
  5. 5.
    Du J, Jin W, Cai Z, Zhu F, Wu Z (2016) A new feature evaluation algorithm and its application to fault of high-speed railway. In: Proceedings of the Second International Conference on Intelligent Transportation, Smart Innovation, Systems and Technologies. Springer, Singapore, pp 1–14.
  6. 6.
    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.
  7. 7.
    He K, Zhang X, Ren S, Sun J (2016) Identity mappings in deep residual networks. In: Leibe B, Matas J, Sebe N, Welling M (eds) Computer Vision – ECCV 2016, Lecture Notes in Computer Science, pp 630–645. Springer International PublishingGoogle Scholar
  8. 8.
    Hu H, Tang B, Gong X, Wei W, Wang H (2017) Intelligent fault diagnosis of the high-speed train with big data based on deep neural networks. IEEE Trans Ind Inf 13(4):2106–2116. CrossRefGoogle Scholar
  9. 9.
    Huang H, Baddour N, Liang M (2018) Bearing fault diagnosis under unknown time-varying rotational speed conditions via multiple time-frequency curve extraction. J Sound Vib 414:43–60. CrossRefGoogle Scholar
  10. 10.
    Iglesias EL, Thompson DJ, Smith M, Kitagawa T, Yamazaki N (2017) Anechoic wind tunnel tests on high-speed train bogie aerodynamic noise. International Journal of Rail Transportation 5(2):87–109. CrossRefGoogle Scholar
  11. 11.
    Janssens O, Slavkovikj V, Vervisch B, Stockman K, Loccufier M, Verstockt S, Van de Walle R, Van Hoecke S (2016) Convolutional neural network based fault detection for rotating machinery. J Sound Vib 377(Supplement C):331–345. CrossRefGoogle Scholar
  12. 12.
    Jia F, Lei Y, Lin J, Zhou X, Lu N (2016) Deep neural networks: a promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data. Mech Syst Signal Process 72–73(Supplement C):303–315. CrossRefGoogle Scholar
  13. 13.
    Jing L, Wang T, Zhao M, Wang P (2017) An adaptive multi-sensor data fusion method based on deep convolutional neural networks for fault diagnosis of planetary gearbox. Sensors 17 (2):414. CrossRefGoogle Scholar
  14. 14.
    Karim F, Majumdar S, Darabi H, Chen S (2017) LSTM Fully Convolutional Networks for Time Series Classification. arXiv:1709.05206 [cs, stat]
  15. 15.
    Karim F, Majumdar S, Darabi H, Harford S (2018) Multivariate LSTM,-FCNs for Time Series Classification. arXiv:1801.04503 [cs, stat]
  16. 16.
    Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In: Proceedings of the 25th International Conference on Neural Information Processing Systems - Volume 1, NIPS’12. Curran Associates Inc., USA, pp 1097–1105Google Scholar
  17. 17.
    LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444. CrossRefGoogle Scholar
  18. 18.
    Li Z, Jiang Y, Guo Q, Hu C, Peng Z (2018) Multi-dimensional variational mode decomposition for bearing-crack detection in wind turbines with large driving-speed variations. Renew Energy 116:55–73. CrossRefGoogle Scholar
  19. 19.
    Li Z, Jiang Y, Hu C, Peng Z (2016) Recent progress on decoupling diagnosis of hybrid failures in gear transmission systems using vibration sensor signal: A review. Measurement 90:4–19. CrossRefGoogle Scholar
  20. 20.
    Lin M, Chen Q, Yan S (2013) Network In Network. arXiv:1312.4400 [cs]
  21. 21.
    Liu C, Zhu L, Ni C (2018) Chatter detection in milling process based on VMD and energy entropy. Mech Syst Signal Process 105:169–182. CrossRefGoogle Scholar
  22. 22.
    Lu Y, Xiang P, Dong P, Zhang X, Zeng J (2018) Analysis of the effects of vibration modes on fatigue damage in high-speed train bogie frames. Eng Fail Anal 89:222–241. CrossRefGoogle Scholar
  23. 23.
    Qin N, Jin WD, Huang J, Jiang P, Li ZM (2013) High speed train bogie fault signal analysis based on wavelet entropy feature. Adv Mater Res 753-755:2286–2289. CrossRefGoogle Scholar
  24. 24.
    Sifre L, Mallat P (2014) Rigid-motion scattering for image classification. PhD Thesis CiteseerGoogle Scholar
  25. 25.
    Silva AA, Gupta S, Bazzi AM, Ulatowski A (2018) Wavelet-based information filtering for fault diagnosis of electric drive systems in electric ships. ISA Trans 78:105–115. CrossRefGoogle Scholar
  26. 26.
    Szegedy C, Ioffe S, Vanhoucke V, Alemi AA Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning p. 7Google Scholar
  27. 27.
    Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 1–9.
  28. 28.
    Verstraete D, Ferrada A, Droguett EL, Meruane V, Modarres M (2017) Deep Learning Enabled Fault Diagnosis Using Time-Frequency Image Analysis of Rolling Element Bearings shock and vibration.
  29. 29.
    Wang X, Yang Z, Yan X (2018) Novel particle swarm optimization-based variational mode decomposition method for the fault diagnosis of complex rotating machinery. IEEE/ASME Trans Mechatron 23(1):68–79. CrossRefGoogle Scholar
  30. 30.
    Wang Y, Markert R, Xiang J, Zheng W (2015) Research on variational mode decomposition and its application in detecting rub-impact fault of the rotor system. Mech Syst Signal Process 60(Supplement C):243–251. CrossRefGoogle Scholar
  31. 31.
    Wu Z, Jin W, Qin N (2016) Fault feature analysis of high-speed train suspension system based on multivariate multi-scale sample entropy. In: 2016 35Th Chinese Control Conference (CCC), pp 3913–3918.
  32. 32.
    Xu K, Guo S, Cao N, Gotz D, Xu A, Qu H, Yao Z, Chen Y (2018) ECGLens: interactive visual exploration of large scale ECG data for arrhythmia detection. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, CHI ’18. ACM, New York, pp 663:1–663:12.
  33. 33.
    Zhang X, Liang Y, Zhou J, zang Y (2015) A novel bearing fault diagnosis model integrated permutation entropy, ensemble empirical mode decomposition and optimized SVM. Measurement 69:164–179. CrossRefGoogle Scholar
  34. 34.
    Zhao Y, Guo ZH, Yan JM (2017) Vibration signal analysis and fault diagnosis of bogies of the high-speed train based on deep neural networks. Journal of Vibroengineering 19(4):2456–2474. CrossRefGoogle Scholar
  35. 35.
    Zheng J, Pan H, Cheng J (2017) Rolling bearing fault detection and diagnosis based on composite multiscale fuzzy entropy and ensemble support vector machines. Mech Syst Signal Process 85:746–759. CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Electrical EngineeringSouthwest Jiaotong UniversityChengduChina

Personalised recommendations