Adversarial Domain Adaptation Fault Diagnosis Method Based on Self-attention Graph Convolutional Network
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Published
Jan 13, 2026
Bo Zhang
shuai su
Ning Ma
Yingxue Wang
Wei Li
Abstract
Intelligent fault diagnosis has made significant progress with the advancements in deep learning and big data. However, the assumption of identical training and testing data distributions often fails in dynamic industrial environments, leading to performance degradation. To address this issue, we propose an Adversarial Domain Adaptation Fault Diagnosis Model Based on Self-attention Graph Convolutional Network (ADA-SAG). The model employs the k-nearest neighbors algorithm to construct graph structures that capture faultinstance relationships across source and target domains. A self-attention enhanced graph convolutional network extracts critical features, while a dual-classifier framework, combined with adversarial learning and maximum mean discrepancy regularization, ensures domain-invariant feature alignment. Experimental results on two benchmark datasets show that the proposed model achieves higher accuracy and robustness
compared to existing methods, making it suitable for diverse
operating conditions. Ablation studies further validate the
contributions of each component to the overall effectiveness
of the model.
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Keywords
Intelligent fault diagnosis,domain adapta- tion (DA),graph convolutional network
References
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Zhang, B., Dong, H., Qaid, H. A., & Wang, Y. (2024). Deep domain adaptation with correlation alignment and supervised contrastive learning for intelligent fault diagnosis in bearings and gears of rotating machinery. In Actuators (Vol. 13, p. 93).
Zhao, C., Liu, G., Shen, W., & Gao, L. (2021a). A multi-representation-based domain adaptation network for fault diagnosis. Measurement, 182, 109650. doi: 10.1016/J.MEASUREMENT.2021.109650
Zhao, C., Liu, G., Shen, W., & Gao, L. (2021b). A multi-representation-based domain adaptation network for fault diagnosis. Measurement, 182, 109650.
Zhu, W., Shi, B., Feng, Z., & Tang, J. (2023). An unsupervised domain adaptation method for intelligent bearing fault diagnosis based on signal reconstruction by cycle-consistent adversarial learning. IEEE Sensors Journal.
Chen, L., Li, Q., Shen, C., Zhu, J., Wang, D., & Xia, M. (2021). Adversarial domain-invariant generalization: A generic domain-regressive framework for bearing fault diagnosis under unseen conditions. IEEE Transactions on Industrial Informatics, 18, 1790-1800. doi: 10.1109/TII.2021.3078712
Chen, Y., Tao, L., Liu, X., Ma, J., Lu, C., & Liu, H. (2023). Open-set fault recognition and inference for rolling bearing based on open fault semantic subspace. IEEE Transactions on Instrumentation and Measurement.
Chen, Z., Yu, W., Ding, X., Shao, Y., & Mechefske, C. K. (2022). Pair-wise orthogonal classifier based domain adaptation network for fault diagnosis in rotating machinery. IEEE Sensors Journal, 22(12), 12086–12097.
Chen, Z., Yu, W., Wang, L., Ding, X., Huang, W., & Shao, Y. (2023). A dual-view style mixing network for unsupervised cross-domain fault diagnosis with imbalanced data. Knowledge-Based Systems, 278, 110918.
Deng, Z., Zhou, K., Li, D., He, J., Song, Y.-Z., & Xiang, T. (2022). Dynamic instance domain adaptation. IEEE Transactions on Image Processing, 31, 4585–4597.
Du, L., Tan, J., Yang, H., Feng, J., Xue, X., Zheng, Q., . . . Zhang, X. (2019). Ssf-dan: Separated semantic feature based domain adaptation network for semantic segmentation. In Proceedings of the ieee/cvf international conference on computer vision (pp. 982–991).
Ganin, Y., & Lempitsky, V. (2015). Unsupervised domain adaptation by backpropagation. In International conference on machine learning (pp. 1180–1189).
Gong, C., Yu, J., & Xia, R. (2020). Unified feature and instance based domain adaptation for aspect-based sentiment analysis. In Proceedings of the 2020 conference on empirical methods in natural language processing (emnlp) (pp. 7035–7045).
Gretton, A., Sejdinovic, D., Strathmann, H., Balakrishnan, S., Pontil, M., Fukumizu, K., & Sriperumbudur, B. K. (2012). Optimal kernel choice for large-scale two-sample tests. Advances in neural information processing systems, 25.
Guo, L., Pfohl, S., Fries, J., Johnson, A. E. W., Posada, J., Aftandilian, C., . . . Sung, L. (2021). Evaluation of domain generalization and adaptation on improving model robustness to temporal dataset shift in clinical medicine. Scientific Reports, 12. doi: 10.1038/s41598-022-06484-1
Li, H., Liu, H., Busch, H. V., Grimm, R., Huisman, H., Tong, A., . . . Lou, B. (2024). Deep learning-based unsupervised domain adaptation via a unified model for prostate lesion detection using multisite bi-parametric mri datasets. Radiology. Artificial intelligence, e230521. doi: 10.1148/ryai.230521
Li, W., Yuan, Z., Sun, W., & Liu, Y. (2020). Domain adaptation for intelligent fault diagnosis under different working conditions. MATEC Web of Conferences. doi: 10.1051/matecconf/202031903001
Liang, P., Deng, C., Yuan, X., & Zhang, L. (2023). A deep capsule neural network with data augmentation generative adversarial networks for single and simultaneous fault diagnosis of wind turbine gearbox. ISA transactions, 135, 462–475.
Liu, Y., Wang, Y., Chow, T. W., & Li, B. (2022). Deep adversarial subdomain adaptation network for intelligent fault diagnosis. IEEE Transactions on Industrial Informatics, 18(9), 6038–6046.
Long, M., Cao, Y., Wang, J., & Jordan, M. (2015). Learning transferable features with deep adaptation networks. In International conference on machine learning (pp. 97–105).
Lu, W., Fan, H., Zeng, K., Li, Z., & Chen, J. (2022). Self-supervised domain adaptation for cross-domain fault diagnosis. International Journal of Intelligent Systems, 37(12), 10903–10923.
Ma, P., Zhang, H., Fan, W., & Wang, C. (2020). A diagnosis framework based on domain adaptation for bearing fault diagnosis across diverse domains. ISA transactions, 99, 465–478.
Saito, K., Watanabe, K., Ushiku, Y., & Harada, T. (2018). Maximum classifier discrepancy for unsupervised domain adaptation. In Proceedings of the ieee conference on computer vision and pattern recognition (pp. 3723–3732).
Smith, W. A., & Randall, R. B. (2015). Rolling element bearing diagnostics using the case western reserve university data: A benchmark study. Mechanical systems and signal processing, 64, 100–131.
Song, Y., Liu, Z., Wang, J., Tang, R., Duan, G., & Tan, J. (2021). Multiscale adversarial and weighted gradient domain adaptive network for data scarcity surface defect detection. IEEE Transactions on Instrumentation and Measurement, 70, 1–10.
Tang, X., Xu, Y., Sun, X., Liu, Y., Jia, Y., Gu, F., & Ball, A. D. (2023). Intelligent fault diagnosis of helical gearboxes with compressive sensing based non-contact measurements. ISA transactions, 133, 559–574.
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., & Bengio, Y. (2017). Graph attention networks. arXiv preprint arXiv:1710.10903.
Wang, R., Huang, W., Wang, J., Shen, C., & Zhu, Z. (2022). Multisource domain feature adaptation network for bearing fault diagnosis under time-varying working conditions. IEEE Transactions on Instrumentation and Measurement, 71, 1-10. doi: 10.1109/tim.2022.3168903
Yan, W., Huang, L., Xia, L., Gu, S., Yan, F., Wang, Y., & Tao, Q. (2020). Mri manufacturer shift and adaptation: Increasing the generalizability of deep learning segmentation for mr images acquired with different scanners. Radiology. Artificial intelligence, 2 4, e190195. doi: 10.1148/ryai.2020190195
Zhang, B., Dong, H., Qaid, H. A., & Wang, Y. (2024). Deep domain adaptation with correlation alignment and supervised contrastive learning for intelligent fault diagnosis in bearings and gears of rotating machinery. In Actuators (Vol. 13, p. 93).
Zhao, C., Liu, G., Shen, W., & Gao, L. (2021a). A multi-representation-based domain adaptation network for fault diagnosis. Measurement, 182, 109650. doi: 10.1016/J.MEASUREMENT.2021.109650
Zhao, C., Liu, G., Shen, W., & Gao, L. (2021b). A multi-representation-based domain adaptation network for fault diagnosis. Measurement, 182, 109650.
Zhu, W., Shi, B., Feng, Z., & Tang, J. (2023). An unsupervised domain adaptation method for intelligent bearing fault diagnosis based on signal reconstruction by cycle-consistent adversarial learning. IEEE Sensors Journal.
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