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Journal of Mechanical Science and Technology

, Volume 33, Issue 11, pp 5177–5188 | Cite as

A new fault diagnosis method based on convolutional neural network and compressive sensing

  • Yunfei Ma
  • Xisheng JiaEmail author
  • Huajun Bai
  • Guozeng Liu
  • Guanglong Wang
  • Chiming Guo
  • Shuangchuan Wang
Article
  • 21 Downloads

Abstract

Compressive sensing is an efficient machinery monitoring framework, which just needs to sample and store a small amount of observed signal. However, traditional reconstruction and fault detection methods cost great time and the accuracy is not satisfied. For this problem, a 1D convolutional neural network (CNN) is adopted here for fault diagnosis using the compressed signal. CNN replaces the reconstruction and fault detection processes and greatly improves the performance. Since the main information has been reserved in the compressed signal, the CNN is able to extract features from it automatically. The experiments on compressed gearbox signal demonstrated that CNN not only achieves better accuracy but also costs less time. The influencing factors of CNN have been discussed, and we compared the CNN with other classifiers. Moreover, the CNN model was also tested on bearing dataset from Case Western Reserve University. The proposed model achieves more than 90 % accuracy even for 50 % compressed signal.

Keywords

Compressive sensing Fault diagnosis Convolutional neural network Feature extraction Gearbox Bearing 

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Notes

Acknowledgments

This work was supported in part by the National Natural Science Foundation of China under the grants 71871220. I would like to express my gratitude to all those who helped me during the writing of this paper. A special acknowledgment should be shown to my supervisor Professor Xisheng Jia from whose useful instructions I benefited greatly.

References

  1. [1]
    M. J. Shi, R. Z. Luo and Y. H. Fu, Fault diagnosis of rotating machinery based on wavelet and energy feature extraction, J. of Electronic Measurement and Instrumentation, 29 (8) (2015) 1114–1120.Google Scholar
  2. [2]
    J. Rissanen and G. G. Langdon, Arithmetic coding, IBM J. of Research and Development, 23 (2) (1979) 149–162.MathSciNetzbMATHGoogle Scholar
  3. [3]
    R. Gallager, Variations on a theme by Huffman, Transaction on Information Theory, 24 (6) (1978) 668–674.MathSciNetzbMATHGoogle Scholar
  4. [4]
    E. J. Candès, Compressive sampling, Proc. of International Congress of Mathematics, Madrid, Spain (2006) 1433–1452.Google Scholar
  5. [5]
    E. J. Candès and T. Tao, Decoding by linear programming, IEEE Transactions on Information Theory, 51 (12) (2005) 4203–4215.MathSciNetzbMATHGoogle Scholar
  6. [6]
    D. L. Donoho, Compressed sensing, IEEE Transactions on Information Theory, 52 (4) (2006) 1289–1306.Google Scholar
  7. [7]
    J. A. Tropp and S. J. Wright, Computational methods for sparse solution of linear inverse problems, Proceedings of the IEEE, 98 (6) (2010) 948–958.Google Scholar
  8. [8]
    J. A. Tropp and A. C. Gilbert, Signal recovery from random measurements via orthogonal matching pursuit, IEEE Transactions on Information Theory, 53 (12) (2007) 4655–4666.MathSciNetzbMATHGoogle Scholar
  9. [9]
    J. D. Sun, Y. Yu and J. T. Wen, Compressed-sensing reconstruction based on block sparse bayesian learning in bearingcondition monitoring, Sensors, 17 (2017) 1454.Google Scholar
  10. [10]
    X. P. Zhang, N. Q. Hu, L. Hu, L. Chen and Z. Cheng, A bearing fault diagnosis method based on the lowdimensional compressed vibration signal, Advances in Mechanical Engineering, 7 (7) (2015) 1–12.Google Scholar
  11. [11]
    G. Tang et al., Compressive sensing of roller bearing faults via harmonic detection from under-sampled vibration signals, Sensors, 15 (2015) 25648–25662.Google Scholar
  12. [12]
    Y. X. Wang et al., Compressed sparse time-frequency feature representation via compressive sensing and its applications in fault diagnosis, Measurement, 68 (2015) 70–81.Google Scholar
  13. [13]
    G. E. Hinton and R. R. Salakhutdinov, Reducing the dimensionality of data with neural networks, Science, 313 (5786) (2006) 504–507.MathSciNetzbMATHGoogle Scholar
  14. [14]
    A. Krizhevsky, I. Sutskever and G. E. Hinton, ImageNet classification with deep convolutional neural networks, Proc. of International Conference on Neural Information Processing Systems, Lake Tahoe, USA (2012) 1097–1105.Google Scholar
  15. [15]
    C. Szegedy et al., Going deeper with convolutions, Proc. of IEEE Conference on Computer Vision and Pattern Recognition, Boston, USA (2015) 1–9.Google Scholar
  16. [16]
    K. He et al., Deep residual learning for image recognition, Proc. of IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, Nevada, USA (2016) 770–778.Google Scholar
  17. [17]
    T. Ince et al., Real-time motor fault detection by 1D convolutional neural networks, IEEE Transactions on Industrial Electronics, 63 (11) (2016) 7067–7075.Google Scholar
  18. [18]
    Q. C. Zhou et al., Fault diagnosis for rotating machinery based on 1D depth convolutional neural network, J. of Vibration and Shock, 37 (23) (2018) 31–37.Google Scholar
  19. [19]
    C. Z. Wu, P. C. Jiang and F. Z. Feng, Faults diagnosis method for gearboxes based on a 1-D convolutional neural network, J. of Vibration and Shock, 37 (22) (2018) 51–56.Google Scholar
  20. [20]
    L. Wen et al., A new convolutional neural network based data-driven fault diagnosis method, IEEE Transactions on Industrial Electronics, 65 (7) (2017) 5990–5998.Google Scholar
  21. [21]
    H. J. Zhu et al., Machinery fault diagnosis based on shift invariant CNN, J. of Vibration and Shock, 38 (5) (2019) 45–52.Google Scholar
  22. [22]
    S. Chen, D. L. Donoho and M. A. Saunders, Atomic decomposition by basis pursuit, SIAM J. on Scientific Computing, 20 (1) (1999) 33–61.MathSciNetzbMATHGoogle Scholar
  23. [23]
    R. Tibshirani, Regression shrinkage and selection via the lasso, J. of the Royal Statistical Society, 58 (1) (1996) 267–288.MathSciNetzbMATHGoogle Scholar
  24. [24]
    M. A. Davenport et al., Signal processing with compressive measurements, IEEE J. of Selected Topics in Signal Processing, 4 (2) (2010) 445–460.Google Scholar
  25. [25]
    E. J. Candes, The restricted isometry property and its implications for compressed sensing, Comptes Rendus Mathematique, 346 (9) (2008) 589–592.MathSciNetzbMATHGoogle Scholar
  26. [26]
    S. Ji, Y. Xue and L. Carin, Bayesian compressive sensing, IEEE Trans. Signal Process, 56 (6) (2008) 2346–2356.MathSciNetzbMATHGoogle Scholar
  27. [27]
    S. Ji, D. Dunson and L. Carin, Multi-task compressive sensing, IEEE Trans. Signal Process, 57 (1) (2009) 92–106.MathSciNetzbMATHGoogle Scholar
  28. [28]
    Z. Zhang and B. D. Rao, Sparse signal recovery with temporally correlated source vectors using sparse Bayesian learning, IEEE J. of Selected Topics in Signal Processing, 5 (5) (2011) 912–926.Google Scholar
  29. [29]
    S. Babacan, R. Molina and A. Katsaggelos, Bayesian compressive sensing using Laplace priors, IEEE Trans. Image Process, 19 (1) (2010) 53–63.MathSciNetzbMATHGoogle Scholar
  30. [30]
    M. Aharon, M. Elad and A. Bruckstein, K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation, IEEE Transactions on Signal Processing, 54 (11) (2006) 4311–4322.zbMATHGoogle Scholar
  31. [31]
    H. Shao, H. Jiang, X. Zhang and M. Niu, Rolling bearing fault diagnosis using an optimization deep belief network, Measurement Science and Technology, 26 (11) (2015) 115002.Google Scholar
  32. [32]
    Z. W. Shang, X. X. Liao, R. Geng, M. S. Gao and X. Liu, Fault diagnosis method of rolling bearing based on deep belief network, J. of Mechanical Science and Technology, 32 (11) (2018) 5139–5145.Google Scholar
  33. [33]
    Y. Shatnawi and M. Al-Khassaweneh, Fault diagnosis in internal combustion engines using extension neural network, IEEE Transactions on Industrial Electronics, 61 (3) (2014) 1434–1443.Google Scholar
  34. [34]
    S. Jurgen, Deep learning in neural networks: An overview, Neural Networks, 61 (1) (2015) 85–117.Google Scholar
  35. [35]
    Y. Lecun, Y. Bengio and G. Hinton, Deep learning, Nature, 521 (7553) (2015) 436–444.Google Scholar
  36. [36]

Copyright information

© KSME & Springer 2019

Authors and Affiliations

  • Yunfei Ma
    • 1
  • Xisheng Jia
    • 1
    Email author
  • Huajun Bai
    • 1
  • Guozeng Liu
    • 1
  • Guanglong Wang
    • 1
  • Chiming Guo
    • 1
  • Shuangchuan Wang
    • 1
  1. 1.Shijiazhuang CampusArmy Engineering UniversityShijiazhuangChina

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