[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content

Advertisement

Springer Nature Link
Log in

Exploiting graph neural network with one-shot learning for fault diagnosis of rotating machinery

  • Original Article
  • Published:
International Journal of Machine Learning and Cybernetics Aims and scope Submit manuscript

Abstract

Insufficient training data often leads to overfitting, posing a significant challenge in diagnosing faults in mechanical devices, particularly rotating machinery. To address this issue, this paper introduces a novel approach employing a graph neural network (GNN) with one-shot learning for fault diagnosis in rotating machinery. Firstly, the Short-Time Fourier Transform (STFT) is utilized for data preprocessing to convert the one-dimensional data into two-dimensional pictures. Subsequently, Feature extraction a convolutional neural network (CNN) is utilized to perform feature extraction. By introducing the adjacency matrix to explore the spatial information within data, a graph neural network (GNN) method is proposed to achieve the fault classification of rotating machinery with small sample. The method utilizes GNN to process structural information between, transferring the distance metric from Euclidean space to non-Euclidean space. Classification accuracy is thereby improved based on information processing in non-Euclidean space.Experiments were implemented on two datasets to verify the proposed method, including an open dataset of the rolling bearing and an experimental rig of the rotate vector (RV) reducer in an industrial robot. Siamese Net, Matching Net, and sparse auto-encoder with random forest (SAE + RF) wereemployed as the comparisons to further prove the effectiveness of the proposed method. Results indicate that the proposed method outperforms all the comparative methods in both rotating machineries.

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
£29.99 /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 includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

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

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 data that support the findings of this study are available from the corresponding author upon reasonable request.

References

  1. Wei L, Yi KW (2018) Analysis on the present situation and development strategy of industrial robot industry in China. Gas Turbine Accessories 3:260–261

    Google Scholar 

  2. Liao Y, Huang R, Li J et al (2021) Correction to: Dynamic distribution adaptation based transfer network for cross domain bearing fault diagnosis. Chin J Mech Eng 34:73. https://doi.org/10.1186/s10033-021-00592-1

    Article  Google Scholar 

  3. Bao C, Qiu YS (2019) Virtual simulation development of ABB industrial robot technology. Acad J Eng Technol Sci 2(4):12–20

    Google Scholar 

  4. Ali AM, Alzubi JA et al (2023) Artificial neural networks training algorithm integrating invasive weed optimization with diferential evolutionary model. J Ambient Intell Humaniz Comput 14:6017–6025

    Article  Google Scholar 

  5. Alzubi OA, Alzubi JA, Tedmori S et al (2018) Consensus-based combining method for classifier ensembles. Int Arab J Inf Technol 15:76–86

    Google Scholar 

  6. Lu K, Gu JX, Fan H et al (2021) Acoustics based monitoring and diagnostics for the progressive deterioration of helical gearboxes. Chin J Mech Eng 34:82. https://doi.org/10.1186/s10033-021-00603-1

    Article  Google Scholar 

  7. Eren L, Ince T, Kiranvaz S (2019) A generic intelligent bearing fault diagnosis system using compact adaptive 1D CNN classifier. J Signal Process Syst 91(2):179–189

    Article  Google Scholar 

  8. Alzubi JA, Rachna J, Preeti N et al (2020) Deep image captioning using an ensemble of CNN and LSTM based deep neural networks. J Intell Fuzzy Syst 40:5761–5769

    Article  Google Scholar 

  9. Chen H, Hu N, Cheng Z et al (2019) A deep convolutional neural network based fusion method of two-direction vibration signal data for health state identification of planetary gearboxes. Measurement 146:268–278

    Article  Google Scholar 

  10. Yang H, Zhang J, Chen L et al (2019) Fault diagnosis of reciprocating compressor based on convolutional neural networks with multisource raw vibration signals. Math Probl Eng 2019:6921975

    Article  Google Scholar 

  11. Rui Z, Ruqiang Y, Jinjiang W et al (2017) Learning to monitor machine health with convolutional bi-directional LSTM networks. Sensors 17(2):273

    Article  Google Scholar 

  12. Wang H, Li S, Song L et al (2019) A novel convolutional neural network based fault recognition method via image fusion of multi-vibration-signals. Comput Ind 105:182–190

    Article  Google Scholar 

  13. Hailun W, Fei W, Lu Z (2021) Application of variational mode decomposition optimized with improved whale optimization algorithm in bearing failure diagnosis. Alex Eng J 60(5):4689–4699

    Article  Google Scholar 

  14. Kim T, Cha M, Kim H et al (2017) Learning to discover cross-domain relations with generative adversarial networks. In: Proceedings of the 34th international conference on machine learning, vol 70, pp 1857–1865

  15. Zhao R, Yan R, Chen Z et al (2019) Deep learning and its applications to machine health monitoring. Mech Syst Signal Process 115:213–237

    Article  Google Scholar 

  16. Liu R, Meng G, Yang B et al (2017) Dislocated time series convolutional neural architecture: an intelligent fault diagnosis approach for electric machine. IEEE Trans Ind Inform 13(3):1310–1320

    Article  Google Scholar 

  17. Lee YO, Jo J, Hwang J. (2018) Application of deep neural network and generative adversarial network to industrial maintenance: a case study of induction motor fault detection. In: IEEE international conference on Big Data. Big Data IEEE, pp 3248–3253

  18. Plakias S, Boutalis YS (2019) Exploiting the generative adversarial framework for one-class multi-dimensional fault detection. Neurocomputing 332:396–405

    Article  Google Scholar 

  19. Wang Z, Wang J, Wang Y (2018) An intelligent diagnosis scheme based on generative adversarial learning deep neural networks and its application to planetary gearbox fault pattern recognition. Neurocomputing 310:213–222

    Article  Google Scholar 

  20. Mao W, Liu Y, Ding L et al (2019) Imbalanced fault diagnosis of rolling bearing based on generative adversarial network: a comparative study. IEEE Access 7:9515–9530

    Article  Google Scholar 

  21. Vinyals O, Blundell C, Lillicrap T et al (2016) Matching networks for one shot learning. arXiv preprint arXiv:1606.04080

  22. Zhang S, Ye F, Wang B, Habetler T (2021) Few-shot bearing fault diagnosis based on model-agnostic meta-learning. IEEE Trans Ind Appl 57:4754–4764

    Article  Google Scholar 

  23. Wang J, Fang Z, Lang N et al (2017) A multi-resolution approach for spinal metastasis detection using deep Siamese neural networks. Comput Biol Med 84:137–146

    Article  Google Scholar 

  24. Vinyals O, Blundell C, Lillicrap T et al (2016) Matching networks for one shot learning. In: Proceedings of the annual conference on neural information processing system. MIT Press, Cambridge, pp 3630–3638

  25. Li QQ, Hou RC, Ding X (2019) Fault diagnosis of rolling bearing based on improved stacking self encoder. Comput Eng Des 40(7):2064–2070

    Google Scholar 

  26. Eddine CD, Noura MH (2017) Fault estimation for nonlinear discrete time-delay system with actuator and sensor faults using nonlinear ts fuzzy models. In: 5th International Conference on Electrical Engineering-Boumerdes (ICEE-B). IEEE, pp 1–6

  27. Gao HZ, Li L, Chen XG et al (2015) Feature extraction and recognition for rolling element bearing faultutilizing short-time fourier transform and non-negative matrix factorization. Chin J Mech Eng 28:96–105

    Article  Google Scholar 

  28. LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324

    Article  Google Scholar 

  29. Gori M, Monfardini G, Scarselli F (2015) A new model for learning in graph domains. In: Proceedings of the international joint conference on neural networks, vol 2, pp 729–734

  30. Wang S, Chen H (2019) A novel deep learning method for the classification of power quality disturbances using deep convolutional neural network. Appl Energy 235:1126–1140

    Article  Google Scholar 

  31. Scarselli F, Gori M, Tsoi AC (2009) The graph neural network model. IEEE Trans Neural Netw 20(1):61–80

    Article  Google Scholar 

  32. Li Y, Tarlow D, Brockschmidt M, Zemel R (2015) Gated graph sequence neural networks. In: Proceedings of the international conference on learning representations

  33. Oriol V, Charles B, Tim L, Daan W et al (2015) Matching networks for one shot learning. Adv Neural Inf Process Syst 34:3630–3638

    Google Scholar 

  34. Zhang J, Gao RX (2021) Deep learning-driven data curation and model interpretation for smart manufacturing. Chin J Mech Eng 34(1):71. https://doi.org/10.1186/s10033-021-00587-y

    Article  MathSciNet  Google Scholar 

  35. Santoro A, Bartunov S, Botvinick M et al (2016) One-shot learning with memory-augmented neural networks. https://arxiv.org/abs/1606.04080

  36. Koch G, Zemel R, Salakhutdinov R (2020) Siamese neural networks for one-shot image recognition. http://www.cs.cmu.edu/~rsalakhu/papers/oneshot

  37. Garcia V, Bruna J (2017) Few-shot learning with graph neural networks. https://arxiv.org/abs/1605.06065

Download references

Acknowledgements

The Chinese National Natural Science Foundation (51905058), the Chongqing Municipal Education Commission's Science and Technology Research Program (KJZD-K202100804), the Venture & Innovation Support Program for Chongqing Overseas Returnees (cx2021075), and the Chongqing Technology and Business University's Research Start-Up Funds are all funding sources for this study (1856018).

Author information

Authors and Affiliations

  1. National Research Base of Intelligent Manufacturing Service, Chongqing Technology and Business University, Chongqing, 400067, China

    Shuai Yang & Xu Chen

  2. School of Management Science and Engineering, Chongqing Technology and Business University, Chongqing, 400067, China

    Xu Chen & Yun Bai

  3. School of Mechanical Engineering, Chongqing Technology and Business University, Chongqing, 400067, China

    Yu Wang

  4. Faculty of Science and Technology, University of Algarve, 8005-139, Faro, Portugal

    Ziqiang Pu

Authors
  1. Shuai Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xu Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yu Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yun Bai
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ziqiang Pu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shuai Yang.

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

Yang, S., Chen, X., Wang, Y. et al. Exploiting graph neural network with one-shot learning for fault diagnosis of rotating machinery. Int. J. Mach. Learn. & Cyber. 15, 5279–5290 (2024). https://doi.org/10.1007/s13042-024-02236-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13042-024-02236-x

Keywords