Abstract
In a cloud-edge collaborative environment, diverse equipment types and operating conditions yield disparate data distribution. This negatively impacts fault diagnosis model accuracy and generalization. To tackle this challenge, domain adaptation is proposed as an effective solution. However, traditional methods have limitations in capturing complex industrial equipment data patterns and discriminating domain features. Therefore, this paper proposes a novel domain adaptation method based on adversarial training called TFADAMA. Firstly, a feature extraction network is designed by integrating Convolutional Neural Network (CNN) and Transformer to capture comprehensive time-frequency feature representations with domain invariance. Additionally, the construction of a multi-scale domain discriminator enhances the discriminative power of domain features and alleviates negative transfer effects. Experimental results demonstrate that TFADAMA achieves a significant improvement of approximately 10% compared to baseline methods, with an average diagnostic accuracy of around 97%.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Tang, X., Xu, L., Chen, G.: Research on the rapid diagnostic method of rolling bearing fault based on cloud–edge collaboration. Entropy 24(9), 1277 (2022)
Zhao, K., Hu, J., Shao, H., et al.: Federated multi-source domain adversarial adaptation framework for machinery fault diagnosis with data privacy. Reliab. Eng. Syst. Saf. 236, 109246 (2023)
Zhang, Y., Ren, Z., Zhou, S., et al.: Supervised contrastive learning-based domain adaptation network for intelligent unsupervised fault diagnosis of rolling bearing. IEEE/ASME Trans. Mechatron. 27(6), 5371–5380 (2022)
Cao, Z., Ma, L., Long, M., et al.: Partial adversarial domain adaptation. In: European Conference on Computer Vision (ECCV) 2018, pp. 135–150 (2018)
Zhang, X.Y., He, L., Wang, X.K., et al.: Transfer fault diagnosis based on local maximum mean difference and K-means. Comput. Ind. Eng. 172, 108568 (2022)
Han, B., Li, B., Du, H., et al.: An attention mechanism-guided domain adversarial network for gearbox fault diagnosis under different operating conditions. Trans. Inst. Meas. Control. 46(5), 927–937 (2024)
Ganin, Y., Ustinova, E., Ajakan, H., et al.: Domain-adversarial training of neural networks. J. Mach. Learn. Res. 17(59), 1–35 (2016)
Wang, Y., Yan, J., Wang, J., et al.: A novel hybrid transfer learning approach for small-sample high-voltage circuit breaker fault diagnosis on-site. IEEE Trans. Ind. Appl. 59(4), 4942–4950 (2023)
Peng, X., Peng, T., Yang, C., et al.: Adversarial domain adaptation network with MixMatch for incipient fault diagnosis of PMSM under multiple working conditions. Knowl.-Based Syst. 284, 111331 (2024)
Deng, Y., Huang, D., Du, S., et al.: A double-layer attention based adversarial network for partial transfer learning in machinery fault diagnosis. Comput. Ind. 127, 103399 (2021)
Devlin, J., Chang, M.W., Lee, K., et al.: BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, (2018)
Vaswani, A., Shazeer, N., Parmar, N., et al.: Attention is all you need. Adv. Neural Inform. Process. Syst. 30 (2017)
He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Yu, X., Wang, Y., Liang, Z., et al.: An adaptive domain adaptation method for rolling bearings’ fault diagnosis fusing deep convolution and self-attention networks. IEEE Trans. Instrum. Meas. 72, 1–14 (2023)
Cardenas-Cornejo, J.J., Ibarra-Manzano, M.A., González-Parada, A., et al.: Classification of inter-turn short-circuit faults in induction motors based on quaternion analysis. Measurement 222, 113680 (2023)
Lessmeier, C., Kimotho, J.K., Zimmer, D., et al.: Condition monitoring of bearing damage in electromechanical drive systems by using motor current signals of electric motors: a benchmark data set for data-driven classification. In: PHM Society European Conference, vol. 3(1), pp. 1–9 (2016)
Acknowledgments
This work is supported by the National Natural Science Foundation of China (No. 62162003, 61762008).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Yang, Y., Zhao, L., Chen, N. (2025). Multiscale Adversarial Domain Adaptation Approach for Cloud-Edge Collaborative Fault Diagnosis of Industrial Equipment. In: Cai, Z., Takabi, D., Guo, S., Zou, Y. (eds) Wireless Artificial Intelligent Computing Systems and Applications. WASA 2024. Lecture Notes in Computer Science, vol 14998. Springer, Cham. https://doi.org/10.1007/978-3-031-71467-2_36
Download citation
DOI: https://doi.org/10.1007/978-3-031-71467-2_36
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-71466-5
Online ISBN: 978-3-031-71467-2
eBook Packages: Computer ScienceComputer Science (R0)