Skip to main content

Advertisement

Springer Nature Link
Log in

Enhanced generative adversarial networks for bearing imbalanced fault diagnosis of rotating machinery

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Traditional rolling bearing fault diagnosis approaches require a large amount of fault data in advance, while some specific fault data is difficult to obtain in engineering scenarios. This imbalanced fault data problem seriously affects the accuracy of fault diagnosis. To improve the accuracy under imbalanced data conditions, we propose a novel data augmentation method of Enhanced Generative Adversarial Networks with Data Selection Module (EGAN-DSM). Firstly, a network enhancement module is designed, which quantifies antagonism between the generator and discriminator through loss value. And the module determines whether to iteratively enhance the networks with weak adversarial ability. Secondly, a Data Selected Module (DSM) is constructed using Hilbert space distance for screening generated data, and the screened data is mixed with original imbalanced data to reconstruct balanced data sets. Then, Deep Convolutional Neural Networks with Wide First-layer Kernels (WDCNN) is used for fault diagnosis. Finally, the method is verified by data measured on a rotating machine experimental platform. The results show that our method has high fault diagnosis accuracy under the condition of imbalanced data.

Graphical abstract

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Institutional subscriptions

Algorithm 1
Fig. 1
Fig. 2
Fig. 3
Algorithm 2
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Data Availability

The HENU datasets generated during the current study are available from the corresponding author on reasonable request. The CWRU datasets can be accessed with https://engineering.case.edu/bearingdatacenter/download-data-file

References

  1. Deng W, Li Z, Li X et al (2022) Compound fault diagnosis using optimized mckd and sparse representation for rolling bearings. IEEE Trans Instrum Meas. 71:1–9. https://doi.org/10.1109/TIM.2022.3159005

    Article  Google Scholar 

  2. Ma J, Shang J, Zhao X et al (2022) Bayes-dcgru with bayesian optimization for rolling bearing fault diagnosis. Appl Intell. 52:11,172–11,183. https://doi.org/10.1007/s10489-021-02924-z

  3. Shao H, Xia M, Wan J et al (2022) Modified stacked autoencoder using adaptive morlet wavelet for intelligent fault diagnosis of rotating machinery. IEEE/ASME Transactions on Mechatronics 27(1):24–33. https://doi.org/10.1109/TMECH.2021.3058061

    Article  Google Scholar 

  4. Wang Z, Yang J, Guo Y (2022) Unknown fault feature extraction of rolling bearings under variable speed conditions based on statistical complexity measures. Mechanical Systems and Signal Processing 172(108):964. https://doi.org/10.1016/j.ymssp.2022.108964

    Article  Google Scholar 

  5. Bendjama H (2022) Bearing fault diagnosis based on optimal morlet wavelet filter and teager-kaiser energy operator. Journal of the Brazilian Society of Mechanical Sciences and Engineering 44(9):1–23. https://doi.org/10.1007/s40430-022-03688-4

    Article  Google Scholar 

  6. Hoang DT, Kang HJ (2019) A survey on deep learning based bearing fault diagnosis. Neurocomputing 335:327–335. https://doi.org/10.1016/j.neucom.2018.06.078

    Article  Google Scholar 

  7. Liu H, Nie H, Zhang Z et al (2020) Anisotropic angle distribution learning for head pose estimation and attention understanding in human-computer interaction. Neurocomputing 433:310–322. https://doi.org/10.1016/j.neucom.2020.09.068

    Article  Google Scholar 

  8. Li Z, Liu H, Zhang Z et al (2022) Learning knowledge graph embedding with heterogeneous relation attention networks. IEEE Transactions on Neural Networks and Learning Systems 33(8):3961–3973. https://doi.org/10.1109/TNNLS.2021.3055147

    Article  MathSciNet  Google Scholar 

  9. Liu T, Wang J, Yang B et al (2021) Ngdnet: Nonuniform gaussian-label distribution learning for infrared head pose estimation and on-task behavior understanding in the classroom. Neurocomputing 436(4):210–220. https://doi.org/10.1016/j.neucom.2020.12.090

    Article  Google Scholar 

  10. Li X, Li T, Li S et al (2023) Learning fusion feature representation for garbage image classification model in human-robot interaction. Infrared Physics and Technology 128(104):457. https://doi.org/10.1016/j.infrared.2022.104457

    Article  Google Scholar 

  11. Wang X, Mao D, Li X (2021) Bearing fault diagnosis based on vibro-acoustic data fusion and 1d-cnn network. Measurement 173(108):518. https://doi.org/10.1016/j.measurement.2020.108518

    Article  Google Scholar 

  12. Xing S, Lei Y, Wang S et al (2020) Distribution-invariant deep belief network for intelligent fault diagnosis of machines under new working conditions. IEEE Trans Ind Electron. 68(3):2617–2625. https://doi.org/10.1109/TIE.2020.2972461

    Article  Google Scholar 

  13. Cui M, Wang Y, Lin X et al (2021) Fault diagnosis of rolling bearings based on an improved stack autoencoder and support vector machine. IEEE Sensors J. 21(4):4927–4937. https://doi.org/10.1109/JSEN.2020.3030910

  14. Liu H, Fang S, Zhang Z et al (2022) Mfdnet: Collaborative poses perception and matrix fisher distribution for head pose estimation. IEEE Transactions on Multimedia 24:2449–2460. https://doi.org/10.1109/TMM.2021.3081873

    Article  Google Scholar 

  15. Liu H, Liu T, Zhang Z et al (2022) Arhpe: Asymmetric relation-aware representation learning for head pose estimation in industrial human-computer interaction. IEEE Transactions on Industrial Informatics 18(10):7107–7117. https://doi.org/10.1109/TII.2022.3143605

    Article  Google Scholar 

  16. Liu H, Zheng C, Li D et al (2022) Edmf: Efficient deep matrix factorization with review feature learning for industrial recommender system. IEEE Trans Ind Inform. 18(7):4361–4371. https://doi.org/10.1109/TII.2021.3128240

    Article  Google Scholar 

  17. Liu H, Zhang C, Deng Y et al (2023) Transifc: Invariant cues-aware feature concentration learning for efficient fine-grained bird image classification. IEEE Transactions on Multimedia pp 1–14. https://doi.org/10.1109/TMM.2023.3238548

  18. Liu H, Liu T, Chen Y, et al (2022) Ehpe: Skeleton cues-based gaussian coordinate encoding for efficient human pose estimation. IEEE Transactions on Multimedia, pp 1–12. https://doi.org/10.1109/TMM.2022.3197364

  19. Huang K, Wu S, Li F et al (2022) Fault diagnosis of hydraulic systems based on deep learning model with multirate data samples. IEEE Trans Neural Netw Learn Syst. 33(11):6789–6801. https://doi.org/10.1109/TNNLS.2021.3083401

    Article  Google Scholar 

  20. Zareapoor M, Shamsolmoali P, Yang J (2021) Oversampling adversarial network for class-imbalanced fault diagnosis. Mechanical Systems and Signal Processing 149(107):175. https://doi.org/10.1016/j.ymssp.2020.107175

    Article  Google Scholar 

  21. Yan K, Su J, Huang J et al (2022) Chiller fault diagnosis based on vae-enabled generative adversarial networks. IEEE Trans Autom Sci Eng. 19(1):387–395. https://doi.org/10.1109/TASE.2020.3035620

    Article  Google Scholar 

  22. Zang Y, Zhang Z, Zhao X et al (2022) Bearing fault diagnosis method based on vae gan and flcnn unbalanced samples. J Vib Shock. 41:199–209. https://doi.org/10.13465/j.cnki.jvs.2022.09.026

  23. Zhang T, Chen J, Li F et al (2022) Intelligent fault diagnosis of machines with small and imbalanced data: A state-of-the-art review and possible extensions. ISA Transactions 119:152–171. https://doi.org/10.1016/j.isatra.2021.02.042

    Article  Google Scholar 

  24. Chawla NV, Bowyer KW, Hall LO et al (2002) Smote: synthetic minority oversampling technique. J artif intell res. 16:321–357. https://doi.org/10.1613/jair.953

    Article  MATH  Google Scholar 

  25. Han H, Wang WY, Mao BH (2005) Borderline-smote: a new over-sampling method in imbalanced data sets learning. In: Int conf intell comput. Springer, pp 878–887,https://doi.org/10.1007/11538059_91

  26. He H, Bai Y, Garcia EA, et al (2008) Adasyn: Adaptive synthetic sampling approach for imbalanced learning. In: 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), pp 1322–1328, https://doi.org/10.1109/IJCNN.2008.4633969

  27. Li Z, Zheng T, Wang Y et al (2021) A novel method for imbalanced fault diagnosis of rotating machinery based on generative adversarial networks. IEEE Trans Instrum Meas. 70:1–17. https://doi.org/10.1109/TIM.2020.3009343

  28. Pan T, Chen J, Zhang T et al (2022) Generative adversarial network in mechanical fault diagnosis under small sample: A systematic review on applications and future perspectives. ISA Transactions. 128:1–10. https://doi.org/10.1016/j.isatra.2021.11.040

  29. Zhu J, Jiang M, Liu Z (2022) Fault detection and diagnosis in industrial processes with variational autoencoder: A comprehensive study. Sensors 22(1):227. https://doi.org/10.3390/s22010227

    Article  Google Scholar 

  30. Goodfellow I, Pouget-Abadie J, Mirza M et al (2020) Generative adversarial networks. Communications of the ACM. 63(11):139–144. https://doi.org/10.1145/3422622

  31. Zhao D, Liu S, Gu D et al (2019) Enhanced data-driven fault diagnosis for machines with small and unbalanced data based on variational auto-encoder. Meas Sci Technol. 31(3):035,004. https://doi.org/10.1088/1361-6501/ab55f8

  32. Li M, Zou D, Luo S et al (2022) A new generative adversarial network based imbalanced fault diagnosis method. Measurement 194(111):045. https://doi.org/10.1016/j.measurement.2022.111045

    Article  Google Scholar 

  33. Liu J, Zhang C, Jiang X (2022) Imbalanced fault diagnosis of rolling bearing using improved msr-gan and feature enhancement- driven capsnet. Mech Syst Signal Proc. 168(108):664. https://doi.org/10.1016/j.ymssp.2021.108664

  34. Shao S, Wang P, Yan R (2019) Generative adversarial networks for data augmentation in machine fault diagnosis. Computers in Industry 106:85–93. https://doi.org/10.1016/j.compind.2019.01.001

    Article  Google Scholar 

  35. Zhang W, Li X, Jia XD et al (2020) Machinery fault diagnosis with imbalanced data using deep generative adversarial networks. Measurement 152(107):377. https://doi.org/10.1016/j.measurement.2019.107377

  36. Hao D, Gao X, Qi W (2022) Data augmentation method based on improved generative adversarial network for the sucker rod pump system. Int J Control, Autom Syst. 20(11):3718–3730. https://doi.org/10.1007/s12555-021-0691-y

    Article  Google Scholar 

  37. Yang G, Zhong Y, Yang L et al (2021) Fault diagnosis of harmonic drive with imbalanced data using generative adversarial network. IEEE Trans Instrum Meas. 70:1–11. https://doi.org/10.1109/TIM.2021.3089240

    Article  Google Scholar 

  38. Li X, Zhang W, Ding Q et al (2019) Multilayer domain adaptation method for rolling bearing fault diagnosis. Signal Processing 157:180–197. https://doi.org/10.1016/j.sigpro.2018.12.005

    Article  Google Scholar 

  39. Gao X, Deng F, Yue X (2020) Data augmentation in fault diagnosis based on the wasserstein generative adversarial network with gradient penalty. Neurocomputing 396:487–494. https://doi.org/10.1016/j.neucom.2018.10.109

    Article  Google Scholar 

  40. Arjovsky M, Chintala S, Bottou L (2017) Wasserstein generative adversarial networks. In: International conference on machine learning, PMLR, pp 214–223, https://dl.acm.org/doi/abs/10.5555/3305381.3305404

  41. Gulrajani I, Ahmed F, Arjovsky M et al (2017) Improved training of wasserstein gans. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. Curran Associates Inc, p 5769–5779, https://dl.acm.org/doi/10.5555/3295222.3295327

  42. Zhang W, Peng G, Li C 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(2):425. https://doi.org/10.3390/s17020425

    Article  Google Scholar 

  43. Gretton A, Borgwardt KM, Rasch MJ et al (2012) A kernel two-sample test. The J Mach Lear Res. 13(1):723–773. https://dl.acm.org/doi/10.5555/2188385.2188410

  44. He Q, Pang Y, Jiang G et al (2021) A spatiotemporal multiscale neural network approach for wind turbine fault diagnosis with imbalanced scada data. IEEE Trans Ind Inform. 17(10):6875–6884. https://doi.org/10.1109/TII.2020.3041114

    Article  Google Scholar 

  45. Zhi Z, Liu L, Liu D et al (2022) Fault detection of the harmonic reducer based on cnnlstm with a novel denoising algorithm. IEEE Sensors Journal 22(3):2572–2581. https://doi.org/10.1109/JSEN.2021.3137992

    Article  Google Scholar 

  46. Liu R, Wang F, Yang B et al (2020) Multiscale kernel based residual convolutional neural network for motor fault diagnosis under nonstationary conditions. IEEE Trans Ind Inform. 16(6):3797–3806. https://doi.org/10.1109/TII.2019.2941868

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (No.61374134) and in part by the Postgraduate Cultivating Innovation and Quality Improvement Action Plan of Henan University (No.SYLYC2022081).

Author information

Authors and Affiliations

  1. School of Artificial Intelligence, Henan University, 450046, Zhengzhou, China

    Yandong Hou, Jiulong Ma, Jinjin Wang & Tianzhi Li

  2. School of Computer and Information Engineering, Henan University, 475004, Kaifeng, China

    Zhengquan Chen

Authors
  1. Yandong Hou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jiulong Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jinjin Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tianzhi Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zhengquan Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Yandong Hou: Methodology, Software, Investigation. Jiulong Ma: Methodology, Conceptualization, Supervision, Writing original draft, Software, Investigation. Zhengquan Chen: Validation, Formal analysis, Writing-review and editing. Jinjin Wang: Graphing, Writing-review and editing. Tianzhi Li: Validation, Writing-review and editing.

Corresponding author

Correspondence to Zhengquan Chen.

Ethics declarations

Conflicts of interest

The authors have no financial or proprietary interests in any material discussed in this article.

Ethical standard

The data used in this paper has no potential conflicts of interest. And the data used have obtained permission to use.

Additional information

Publisher's Note

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

Yandong Hou, Jiulong Ma and Zhengquan Chen contributed equally to this work.

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

Hou, Y., Ma, J., Wang, J. et al. Enhanced generative adversarial networks for bearing imbalanced fault diagnosis of rotating machinery. Appl Intell 53, 25201–25215 (2023). https://doi.org/10.1007/s10489-023-04870-4

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-023-04870-4

Keywords

Search

Navigation