Multi-Branch Joint Time-Frequency Transformer for Domain Generalization Fault Diagnosis of Rotating Machinery
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Published
Jan 13, 2026
Qitong Chen
Liang Chen Hong Zhuang Qi Li Wenjing Zhou
https://orcid.org/0000-0001-8970-1345
Liang Chen Hong Zhuang Qi Li Wenjing Zhou
Abstract
Conventional time-frequency Transformers primarily focus on the global features of signals in the time-frequency domain while neglecting the local features in both the time domain and frequency domain. This limitation hinders the ability of the model to effectively capture the shared features among time, frequency, and time-frequency representations. To address this issue, a Multi-Branch Joint Time-Frequency Transformer (MBJTF-Transformer) is proposed for domain generalization (DG) fault diagnosis of rotating machinery. Specifically, a time-branch Transformer is designed to extract temporal features, while a frequency-branch Transformer captures frequency-domain information. In addition, a time-frequency Transformer is employed to learn the shared representations across time, frequency, and time-frequency domains. Finally, a multi-decision fusion strategy of MBJTF-Transformer is adopted to enhance the generalization capability of the model. Experimental results on both the SCARA (Selective Compliance Assembly Robot Arm, SCARA) dataset and the PU (Paderborn University) bearing dataset demonstrate that the proposed MBJTF-Transformer achieves superior DG performance compared to multiple state-of-the-art sequential models.
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Keywords
Time-Frequency Transformer, Domain Generalization, Multi-decision Fusion, Joint Optimization
References
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Chen, Y., Zhang, D., Yan, R., & Xie, M. (2025). Applications of domain generalization to machine fault diagnosis: A survey. IEEE/CAA Journal of Automatica Sinica.
Ding, Y., Jia, M., Miao, Q., & Cao, Y. (2022). A novel time– frequency transformer based on self–attention mechanism and its application in fault diagnosis of rolling bearings. Mechanical Systems and Signal Processing, 168, 108616.
Huang, H.,Wang, R., Zhou, K., Ning, L., & Song, K. (2023). Causalvit: Domain generalization for chemical engineering process fault detection and diagnosis. Process Safety and Environmental Protection, 176, 155–165.
Kitaev, N., Kaiser, Ł., & Levskaya, A. (2020). Reformer: The efficient transformer. arXiv preprint
arXiv:2001.04451.
Lessmeier, C., Kimotho, J. K., Zimmer, D., & Sextro, W. (2016). 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).
Li, K., Wang, C., & Wu, H. (2023). Multimodal transformer for bearing fault diagnosis: A new method based on frequency-time feature decomposition.
Liu, S., Chen, J., He, S., Shi, Z., & Zhou, Z. (2023). Few-shot learning under domain shift: Attentional contrastive calibrated transformer of time series for fault diagnosis under sharp speed variation. Mechanical systems and signal processing, 189, 110071.
Ma, H., Wei, J., Zhang, G., Kong, X., & Du, J. (2024). Causality-inspired multi-source domain generalization method for intelligent fault diagnosis under unknown operating conditions. Reliability Engineering & System Safety, 252, 110439.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., . . . Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30.
Wang, J., Ren, H., Shen, C., Huang, W., & Zhu, Z. (2024). Multi-scale style generative and adversarial contrastive networks for single domain generalization fault diagnosis. Reliability Engineering & System Safety, 243, 109879.
Woo, G., Liu, C., Sahoo, D., Kumar, A., & Hoi, S. (2022). Etsformer: Exponential smoothing transformers for time-series forecasting. arXiv preprint arXiv:2202.01381.
Wu, H., Xu, J., Wang, J., & Long, M. (2021). Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting. Advances in neural information processing systems, 34, 22419–22430.
Xiao, Y., Shao, H., Wang, J., Yan, S., & Liu, B. (2024). Bayesian variational transformer: A generalizable model for rotating machinery fault diagnosis. Mechanical Systems and Signal Processing, 207, 110936.
Zeng, A., Chen, M., Zhang, L., & Xu, Q. (2023). Are transformers effective for time series forecasting? In Proceedings of the aaai conference on artificial intelligence (Vol. 37, pp. 11121–11128).
Zhang, T., Zhang, Y., Cao, W., Bian, J., Yi, X., Zheng, S., & Li, J. (2022). Less is more: Fast multivariate time series forecasting with light sampling-oriented mlp structures. Retrieved from https://arxiv.org/abs/2207.01186.
Zhao, C., & Shen, W. (2024). Imbalanced domain generalization via semantic-discriminative augmentation for intelligent fault diagnosis. Advanced Engineering Informatics, 59, 102262.
Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., & Zhang, W. (2021). Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the aaai conference on artificial intelligence (Vol. 35, pp. 11106–11115).
Zhou, T., Ma, Z., Wen, Q., Wang, X., Sun, L., & Jin, R. (2022). Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting. In International conference on machine learning (pp. 27268–27286).
Section
Regular Session Papers
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