Intelligent fault detection using raw vibration signals via dilated convolutional neural networks

Abstract

Fault detection and diagnosis is critical to improve the reliability and availability in induction motors (IMs). Machine learning and deep learning techniques have been widely used in induction motor fault detection and diagnosis. In this paper, we propose a new deep learning model based on a dilated convolutional neural network (D-CNN) for detecting bearing faults in IMs. The proposed model works directly on raw vibration signals without any hand-crafted feature extraction process. Our model can incorporate global context without losing important local information by stacking dilated convolutions with an increasing width. Numerical results show that the proposed D-CNN is not only capable of classifying normal signals perfectly but also can achieve higher accuracy than conventional techniques under noisy environments.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

References

  1. 1.

    Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, Corrado GS, Davis A, Dean J, Devin M, Ghemawat S, Goodfellow I, Harp A, Irving G, Isard M, Jia Y, Jozefowicz R, Kaiser L, Kudlur M, Levenberg J, Mané D, Monga R, Moore S, Murray D, Olah C, Schuster M, Shlens J, Steiner B, Sutskever I, Talwar K, Tucker P, Vanhoucke V, Vasudevan V, Viégas F, Vinyals O, Warden P, Wattenberg M, Wicke M, Yu Y, Zheng X (2015) TensorFlow: large-scale machine learning on heterogeneous systems. https://www.tensorflow.org/. Software available from tensorflow.org

  2. 2.

    Bellini A, Filippetti F, Tassoni C, Capolino GA (2008) Advances in diagnostic techniques for induction machines. IEEE Trans Ind Electron 55(12):4109–4126. https://doi.org/10.1109/TIE.2008.2007527

    Article  Google Scholar 

  3. 3.

    Benbouzid MEH (2000) A review of induction motors signature analysis as a medium for faults detection. IEEE Trans Ind Electron 47(5):984–993. https://doi.org/10.1109/41.873206

    Article  Google Scholar 

  4. 4.

    Bianchini C, Immovilli F, Cocconcelli M, Rubini R, Bellini A (2011) Fault detection of linear bearings in brushless ac linear motors by vibration analysis. IEEE Trans Ind Electron 58(5):1684–1694. https://doi.org/10.1109/TIE.2010.2098354

    Article  Google Scholar 

  5. 5.

    Bouzida A, Touhami O, Ibtiouen R, Belouchrani A, Fadel M, Rezzoug A (2011) Fault diagnosis in industrial induction machines through discrete wavelet transform. IEEE Trans Ind Electron 58(9):4385–4395. https://doi.org/10.1109/TIE.2010.2095391

    Article  Google Scholar 

  6. 6.

    Cha YJ, Choi W, Bykztrk O (2017) Deep learning-based crack damage detection using convolutional neural networks. Comput Aided Civ Infrastruct Eng 32(5):361–378. https://doi.org/10.1111/mice.12263

    Article  Google Scholar 

  7. 7.

    Chen X, Sun L, Zhu H, Zhen Y, Chen H (2012) Application of internet of things in power-line monitoring. In: 2012 International Conference on Cyber-enabled Distributed Computing and Knowledge Discovery. IEEE. https://doi.org/10.1109/cyberc.2012.77

  8. 8.

    Chollet F et al (2015) Keras. https://github.com/fchollet/keras. Accessed 30 Aug 2018

  9. 9.

    Dou W, Li Y (2018) A fault-tolerant computing method for xdraw parallel algorithm. J Supercomput 74(6):2776–2800. https://doi.org/10.1007/s11227-018-2321-x

    Article  Google Scholar 

  10. 10.

    Duchi J, Hazan E, Singer Y (2011) Adaptive subgradient methods for online learning and stochastic optimization. J Mach Learn Res 12:2121–2159. http://dl.acm.org/citation.cfm?id=1953048.2021068

  11. 11.

    Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S (2017) Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639):115–118. https://doi.org/10.1038/nature21056

    Article  Google Scholar 

  12. 12.

    Hwang DH, Youn YW, Sun JH, Choi KH, Lee JH, Kim YH (2015) Support vector machine based bearing fault diagnosis for induction motors using vibration signals. J Electr Eng Technol 10(4):1558–1565. https://doi.org/10.5370/JEET.2015.10.4.1558

    Article  Google Scholar 

  13. 13.

    Karvelis P, Georgoulas G, Tsoumas IP, Antonino-Daviu JA, Climente-Alarcn V, Stylios CD (2015) A symbolic representation approach for the diagnosis of broken rotor bars in induction motors. IEEE Trans Ind Inform 11(5):1028–1037. https://doi.org/10.1109/TII.2015.2463680

    Article  Google Scholar 

  14. 14.

    Khan MA, Kim Y, Choo J (2018) Intelligent fault detection via dilated convolutional neural networks. In: 2018 IEEE International Conference on Big Data and Smart Computing (BigComp), pp 729–731. https://doi.org/10.1109/BigComp.2018.00137

  15. 15.

    Kia SH, Henao H, Capolino GA (2007) A high-resolution frequency estimation method for three-phase induction machine fault detection. IEEE Trans Ind Electron 54(4):2305–2314. https://doi.org/10.1109/TIE.2007.899826

    Article  Google Scholar 

  16. 16.

    Kim J, Lee W, Song JJ, Lee SB (2017) Optimized combinatorial clustering for stochastic processes. Clust Comput 20(2):1135–1148. https://doi.org/10.1007/s10586-017-0763-1

    Article  Google Scholar 

  17. 17.

    Kim YH, Youn YW, Hwang DH, Sun JH, Kang DS (2013) High-resolution parameter estimation method to identify broken rotor bar faults in induction motors. IEEE Trans Ind Electron 60(9):4103–4117. https://doi.org/10.1109/TIE.2012.2227912

    Article  Google Scholar 

  18. 18.

    Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. CoRR, arXiv:1412.6980

  19. 19.

    Krizhevsky A, Sutskever I, Hinton G (2012) Imagenet classification with deep convolutional neural networks. In: Proceedings of the 25th International Conference on Neural Information Processing Systems—Volume 1, NIPS’12, pp 1097–1105. Curran Associates Inc., USA. http://dl.acm.org/citation.cfm?id=2999134.2999257

  20. 20.

    LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444. https://doi.org/10.1038/nature14539

    Article  Google Scholar 

  21. 21.

    Li B, Chow MY, Tipsuwan Y, Hung JC (2000) Neural-network-based motor rolling bearing fault diagnosis. IEEE Trans Ind Electron 47(5):1060–1069. https://doi.org/10.1109/41.873214

    Article  Google Scholar 

  22. 22.

    Li C, Zhang W, Peng G, Liu S (2018) Bearing fault diagnosis using fully-connected winner-take-all autoencoder. IEEE Access 6:6103–6115. https://doi.org/10.1109/access.2017.2717492

    Article  Google Scholar 

  23. 23.

    Lou X, Loparo KA (2004) Bearing fault diagnosis based on wavelet transform and fuzzy inference. Mech Syst Signal Process 18(5):1077–1095. https://doi.org/10.1016/S0888-3270(03)00077-3. http://www.sciencedirect.com/science/article/pii/S0888327003000773

  24. 24.

    Nandi S, Toliyat HA, Li X (2005) Condition monitoring and fault diagnosis of electrical motors-a review. IEEE Trans Energy Convers 20(4):719–729. https://doi.org/10.1109/TEC.2005.847955

    Article  Google Scholar 

  25. 25.

    v. d. Oord A, Dieleman S, Zen H, Simonyan K, Vinyals O, Graves A, Kalchbrenner N, Senior A, Kavukcuoglu K (2016) Wavenet: a generative model for raw audio. Arxiv arXiv:1609.03499

  26. 26.

    Oquab M, Bottou L, Laptev I, Sivic J (2014) Learning and transferring mid-level image representations using convolutional neural networks. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition. IEEE. https://doi.org/10.1109/cvpr.2014.222

  27. 27.

    Riera-Guasp M, Antonino-Daviu JA, Capolino GA (2015) Advances in electrical machine, power electronic, and drive condition monitoring and fault detection: State of the art. IEEE Trans Ind Electron 62(3):1746–1759. https://doi.org/10.1109/TIE.2014.2375853

    Article  Google Scholar 

  28. 28.

    Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15:1929–1958. http://jmlr.org/papers/v15/srivastava14a.html

  29. 29.

    Strubell E, Verga P, Belanger D, McCallum A (2017) Fast and accurate sequence labeling with iterated dilated convolutions. arXiv:1702.02098

  30. 30.

    Widodo A, Yang BS (2007) Support vector machine in machine condition monitoring and fault diagnosis. Mech Syst Signal Process 21(6):2560–2574. https://doi.org/10.1016/j.ymssp.2006.12.007. http://www.sciencedirect.com/science/article/pii/S0888327007000027

  31. 31.

    Yong B, Zhang G, Chen H, Zhou Q (2016) Intelligent monitor system based on cloud and convolutional neural networks. J Supercomput 73(7):3260–3276. https://doi.org/10.1007/s11227-016-1934-1

    Article  Google Scholar 

  32. 32.

    Yu F, Koltun V (2016) Multiscale context aggregation by dilated convolutions. In: Proceedings of the International Conference on Learning Representations (ICLR)

  33. 33.

    Yu Z, Li T, Luo G, Fujita H, Yu N, Pan Y (2018) Convolutional networks with cross-layer neurons for image recognition. Inf Sci 433–434:241–254. https://doi.org/10.1016/j.ins.2017.12.045

    MathSciNet  Article  Google Scholar 

  34. 34.

    Zeiler MD (2012) Adadelta: an adaptive learning rate method. CoRR, arXiv:1212.5701

  35. 35.

    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. https://doi.org/10.1016/j.ymssp.2017.06.022

    Article  Google Scholar 

  36. 36.

    Zhang W, Peng G, Li C, Chen Y, Zhang Z (2017) A new deep learning model for fault diagnosis with good anti-noise and domain adaptation ability on raw vibration signals. Sensors 17(3):425. https://doi.org/10.3390/s17020425

    Article  Google Scholar 

Download references

Acknowledgements

This research was in part supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (NRF-2017R1C1B1012259) and in part supported by Korea Electric Power Corporation (Grant No. R17XA05-22).

Author information

Affiliations

Authors

Corresponding author

Correspondence to Yong-Hwa Kim.

Additional information

This work is an extended version of [14].

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Khan, M.A., Kim, YH. & Choo, J. Intelligent fault detection using raw vibration signals via dilated convolutional neural networks. J Supercomput 76, 8086–8100 (2020). https://doi.org/10.1007/s11227-018-2711-0

Download citation

Keywords

  • Dilated convolution
  • Intelligent fault detection
  • Vibration signals
  • Deep neural networks
  • Convolutional neural networks