SpringerLink
Log in

A Fault Diagnosis Method of Rotor System Based on Parallel Convolutional Neural Network Architecture with Attention Mechanism

  • Published:
Journal of Signal Processing Systems Aims and scope Submit manuscript

Abstract

In practical engineering applications, the working load of the rotor system is changing constantly, and the noise pollution of its working environment is serious, which leads to the performance degradation of traditional fault diagnosis methods. To solve the above problems, we present a novel rotor system fault diagnosis model based on parallel convolutional neural network architecture with attention mechanism (AMPCNN). The model uses convolution kernels of different sizes in parallel channels to process raw data, and based on late feature fusion, a more comprehensive feature map is obtained. Furthermore, the information sharing between the two channels is realized through the attention mechanism so that the effective features of one channel can be reflected in another channel. The performance of the model under variable working conditions is verified by the Machinery Fault Database (MAFAULDA), and the average accuracy is 99.58%. By dividing Gaussian white noise from -9 dB to 2 dB into 11 intervals and adding it to the public data of Wuhan University, the noise resistance performance is verified, and the proposed method can obtain 100% diagnosis accuracy even in the high noise condition. The above experiments show that in terms of load adaptability and noise immunity, the method has higher accuracy than traditional deep learning classification 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

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9

Similar content being viewed by others

Data Availability

The code and models generated during and/or analyzed during the current study are available in the GitHub, [https://github.com/zhiqan/xuexi].

References

  1. Nath, A. G., Udmale, S. S., & Singh, S. K. (2021). Role of artificial intelligence in rotor fault diagnosis: A comprehensive review. Artificial Intelligence Review, 54, 2609–2668.

    Article  Google Scholar 

  2. Miao, F., Zhao, R. Z., Wang, X. L., et al. (2020). A New Fault Feature Extraction Method for Rotating Machinery Based on Multiple Sensors. Sensors, 20(6), 1713.

    Article  Google Scholar 

  3. Zhang, H. D. (2019). Fault diagnosis and life prediction of mechanical equipment based on artificial intelligence. Journal of Intelligent & Fuzzy Systems, 37(12), 1–10.

    Google Scholar 

  4. Jiao, J. Y., Zhao, M., Lin, J., et al. (2020). A comprehensive review on convolutional neural network in machine fault diagnosis. Neurocomputing, 417, 36–63.

    Article  Google Scholar 

  5. Ince, T., Kiranyaz, S., Eren, L., et al. (2016). Real-time motor fault detection by 1-D convolutional neural networks. IEEE Transactions on Industrial Electronics, 63(11), 7067–7075.

    Article  Google Scholar 

  6. Guo, F. Y., Zhang, Y. C., Wang, Y., et al. (2020). Fault Detection of Reciprocating Compressor Valve Based on One-Dimensional Convolutional Neural Network. Mathematical Problems in Engineering, 2020, 8058723.

    Google Scholar 

  7. Qian, W. W., Li, S. M., Wang, J. R., et al. (2018). An intelligent fault diagnosis framework for raw vibration signals: Adaptive overlap** convolutional neural network. Measurement Science & Technology, 29(9), 095009.

    Article  Google Scholar 

  8. Qiao, H. H., Wang, T. Y., Wang, P., et al. (2019). An Adaptive Weighted Multiscale Convolutional Neural Network for Rotating Machinery Fault Diagnosis Under Variable Operating Conditions. IEEE Access, 7, 118954–118964.

    Article  Google Scholar 

  9. Zhang, W., Peng, G. L., Li, C. H., et al. (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.

    Article  Google Scholar 

  10. Chen, X. H., Zhang, B. K., & Gao, D. (2021). Bearing fault diagnosis base on multi-scale CNN and LSTM model. Journal of Intelligent Manufacturing, 32(4), 971–987.

    Article  Google Scholar 

  11. Nath, A. G., Udmale, S. S., Raghuwanshi, D., et al. (2021). Improved Structural Rotor Fault Diagnosis Using Multi-Sensor Fuzzy Recurrence Plots and Classifier Fusion. IEEE Sensors Journal, 21(19), 21705–21717.

    Article  Google Scholar 

  12. Zhang, X. N., Liu, S. Y., Li, L., et al. (2021). Multiscale holospectrum convolutional neural network-based fault diagnosis of rolling bearings with variable operating conditions. Measurement Science & Technology, 32(10), 105027.

    Article  Google Scholar 

  13. Guo, S., Zhang, B., Yang, T., et al. (2020). Multitask Convolutional Neural Network With Information Fusion for Bearing Fault Diagnosis and Localization. IEEE Transactions on Industrial Electronics, 67(9), 8005–8015.

    Article  Google Scholar 

  14. Shi, Z., Chen, J. L., Zi, Y. Y., et al. (2021). A Novel Multitask Adversarial Network via Redundant Lifting for Multicomponent Intelligent Fault Detection under Sharp Speed Variation. IEEE Transactions on Instrumentation and Measurement, 70, 3511010.

    Article  Google Scholar 

  15. Zhou, J. Y., Yang, X. Y., Zhang, L., et al. (2020). Multisignal VGG19 Network with Transposed Convolution for Rotating Machinery Fault Diagnosis Based on Deep Transfer Learning. Shock and Vibration, 2020, 8863388.

    Article  Google Scholar 

  16. Wang, J. R., Li, S. M., An, Z. H., et al. (2019). Batch-normalized deep neural networks for achieving fast intelligent fault diagnosis of machines. Neurocomputing, 329, 53–65.

    Article  Google Scholar 

  17. Yin, W. P., Schütze, H., **ang, B., et al. (2016). ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs. Transactions of the Association for Computational Linguistics, 4, 259–272.

    Article  Google Scholar 

  18. Marins, M. A., Ribeiro, F. M. L., Netto, S. L., et al. (2018). Improved similarity-based modeling for the classification of rotating-machine failures. Journal of the Franklin Institute, 355(4), 1913–1930.

    Article  MATH  Google Scholar 

  19. Van der Maaten, L., & Hinton, G. (2008). Visualizing Data Using t-SNE. Journal of Machine Learning Research, 9, 2579–2605.

    MATH  Google Scholar 

  20. Zhou, S., **ao, M. H., Bartos, P., et al. (2020). Remaining Useful Life Prediction and Fault Diagnosis of Rolling Bearings Based on Short-Time Fourier Transform and Convolutional Neural Network. Shock and Vibration, 2020, 8857307.

    Article  Google Scholar 

  21. Van den Hoogen, J. O. D., Bloemheuvel, S. D., & Atzmueller, M. (2020). An Improved Wide-Kernel CNN for Classifying Multivariate Signals in Fault Diagnosis. 2020 International Conference on Data Mining Workshops (ICDMW) (pp. 275–283). IEEE, ELECTR NETWORK.

    Chapter  Google Scholar 

  22. Alzghoul, A., Jarndal, A. H., Alsyouf, I., Bingamil, A. A., Ali, M. A., & AlBaiti, S. (2021). On the Usefulness of Pre-processing Methods in Rotating Machines Faults Classification using Artificial Neural Network. Applied and Computational Mechanics, 7, 254–261.

    Google Scholar 

  23. Liu, D., **ao, Z. H., Hu, X., et al. (2019). Feature extraction of rotor fault based on EEMD and curve code. Measurement, 135, 712–724.

    Article  Google Scholar 

  24. Wang, H., Liu, Z., Peng, D., & Qin, Y. (2019). Understanding and learning discriminant features based on multi-attention 1dcnn for wheelset bearing fault diagnosis. IEEE Transactions on Industrial Informatics, 16(9), 5735–5745.

    Article  Google Scholar 

  25. Wen, L., Li, X., & Gao, L. (2019). A transfer convolutional neural network for fault diagnosis based on resnet-50. Neural Computing and Applications, 32(10), 6111–6124.

    Article  Google Scholar 

Download references

Acknowledgements

The authors are very grateful for the support provided by National Natural Science Foundation of China (Grant No. 11972131 and No. 12072089), and the National Science and Technology Major Project (Grant No. 2017-IV-0010-0047).

Author information

Authors and Affiliations

  1. School of Mechatronics Engineering, Harbin Institute of Technology, Harbin, Heilongjiang, 150000, People’s Republic of China

    Zhiqian Zhao, Yinghou Jiao & **ang Zhang

  2. Laboratory of Vibration and Noise Control, Harbin Institute of Technology, Harbin, Heilongjiang, 150000, People’s Republic of China

    Zhiqian Zhao & Yinghou Jiao

Authors
  1. Zhiqian Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yinghou Jiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. **ang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yinghou Jiao.

Ethics declarations

Competing Interests

The authors declare no competing interests.

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

Zhao, Z., Jiao, Y. & Zhang, X. A Fault Diagnosis Method of Rotor System Based on Parallel Convolutional Neural Network Architecture with Attention Mechanism. J Sign Process Syst 95, 965–977 (2023). https://doi.org/10.1007/s11265-023-01846-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11265-023-01846-y

Keywords

Search

Navigation