Partial Domain Adaptation for Intelligent Machinery Fault Diagnosis Leveraging Healthy-Only Target Data for Multi-Class Classification
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
Accurate gearbox fault diagnosis across different operating conditions plays an important role in prognostics and health management. In real industrial scenarios, a common challenge arises when the source domain contains multiple fault classes, while the target domain includes only healthy samples during training. To address this issue, this study proposes a unified industrial fault diagnosis framework designed to handle the partial domain adaptation problem. Specifically, the overall framework involves: a unified data processing pipeline, a robust deep learning architecture for accurate fault classification, and integration of maximum mean discrepancy loss to align feature distributions between source and target domains. Experimental results demonstrate that our proposed partial domain adaptation-based deep learning model significantly outperforms benchmark models, achieving accuracy improvements exceeding 20% across multiple domain adaptation tasks. This study provides a practical solution for intelligent gearbox diagnosis under domain shift constraints.
How to Cite
##plugins.themes.bootstrap3.article.details##
Machine Learning, Deep Learning, PHM, Fault Diagnosis, Smart Manufacturing, Industrial AI
Farahani, A., Voghoei, S., Rasheed, K., & Arabnia, H. R. (2021). A brief review of domain adaptation. Advances in data science and information engineering: proceedings from ICDATA 2020 and IKE 2020, 877–894.
He, X., Zhao, F., Song, N., Su, C., & Liu, P. (2025). A deep-transfer-learning fault diagnosis method for gearboxes based on discriminative feature extraction and improved domain adversarial neural networks. Nondestructive Testing and Evaluation, 1–22.
Jiang, F., Lin, W., Wu, Z., Zhang, S., Chen, Z., & Li, W. (2024). Fault diagnosis of gearbox driven by vibration response mechanism and enhanced unsupervised domain adaptation. Advanced Engineering Informatics, 61, 102460.
Kavianpour, M., Ghorvei, M., Kavianpour, P., Ramezani, A., & Beheshti, M. T. (2022). An intelligent gearbox fault diagnosis under different operating conditions using adversarial domain adaptation. In 2022 8th international conference on control, instrumentation and automation (iccia) (pp. 1–6).
Lee, J., & Su, H. (2024). A unified industrial large knowledge model framework in industry 4.0 and smart manufacturing. International Journal of AI for Materials and Design, 1(2), 41–47.
Lee, J., & Su, H. (2025). Rethinking industrial artificial intelligence: A unified foundation framework. International Journal of AI for Materials and Design, 2(2), 56–68.
Lee, J., Su, H., Ji, D.-Y., & Minami, T. (2025). Engineering artificial intelligence: framework, challenges, and future direction. Machine Learning: Engineering, 1(1), 013001. doi: 10.1088/3049-4761/adce0d
Li, F., Dai, Z., Jiang, L., Song, C., Zhong, C., & Chen, Y. (2024). Prediction of the remaining useful life of bearings through cnn-bi-lstm-based domain adaptation model. Sensors, 24(21), 6906.
Liu, D., Cui, L., Wang, G., & Cheng, W. (2025). Interpretable domain adaptation transformer: a transfer learning method for fault diagnosis of rotating machinery. Structural Health Monitoring, 24(2), 1187–1200.
Liu, H., Chen, W., Chen, W., & Gu, Y. (2022). A cnn-lstm-based domain adaptation model for remaining useful life prediction. Measurement Science and Technology, 33(11), 115118.
Liu, X., Yoo, C., Xing, F., Oh, H., El Fakhri, G., Kang, J.-W., . . . others (2022). Deep unsupervised domain adaptation: A review of recent advances and perspectives. APSIPA Transactions on Signal and Information Processing, 11(1).
Liu, Z., Tan, C., Liu, Y., Li, H., Cui, B., & Zhang, X. (2023). A study of a domain-adaptive lstm-dnn-based method for remaining useful life prediction of planetary gearbox. Processes, 11(7), 2002.
Lu, X., Yao, X., Jiang, Q., Shen, Y., Xu, F., & Zhu, Q. (2025). Remaining useful life prediction model of cross-domain rolling bearing via dynamic hybrid domain adaptation and attention contrastive learning. Computers in Industry, 164, 104172.
Mao, G., Zhang, Z., Qiao, B., & Li, Y. (2022). Fusion domain-adaptation cnn driven by images and vibration signals for fault diagnosis of gearbox cross-working conditions. Entropy, 24(1), 119.
Polverino, L., Abbate, R., Manco, P., Perfetto, D., Caputo, F., Macchiaroli, R., & Caterino, M. (2023). Machine learning for prognostics and health management of industrial mechanical systems and equipment: A systematic literature review. International Journal of Engineering Business Management, 15, 18479790231186848.
Qin, Y., Yao, Q., Wang, Y., & Mao, Y. (2021). Parameter sharing adversarial domain adaptation networks for fault transfer diagnosis of planetary gearboxes. Mechanical Systems and Signal Processing, 160, 107936.
Shi, Y., Ying, X., & Yang, J. (2022). Deep unsupervised domain adaptation with time series sensor data: A survey. Sensors, 22(15), 5507.
Singhal, P., Walambe, R., Ramanna, S., & Kotecha, K. (2023). Domain adaptation: challenges, methods, datasets, and applications. IEEE access, 11, 6973–7020.
Su, H., & Lee, J. (2024). Machine learning approaches for diagnostics and prognostics of industrial systems using open source data from phm data challenges: A review. International Journal of Prognostics and Health Management, 15(2).
Su, H., & Lee, J. (2025). Transfer learning and ensemble learning for fault diagnosis using vibration signals. In 2025 ieee international conference on prognostics and health management (icphm) (pp. 1–9).
Tsui, K. L., Chen, N., Zhou, Q., Hai, Y., & Wang, W. (2015). Prognostics and health management: A review on data driven approaches. Mathematical Problems in Engineering, 2015(1), 793161.
Xu, H., Dai, L., Zhao, P., Liu, X., & He, C. (2024). Manifold embedded ensemble partial domain adaptation: A novel partial-set transfer mechanism for gearboxes fault diagnosis. IEEE Sensors Journal.
Yan, R., Li, W., Lu, S., Xia, M., Chen, Z., Zhou, Z., . . . Lu, J. (2024). Transfer learning for prognostics and health management: Advances, challenges, and opportunities. Journal of Dynamics, Monitoring and Diagnostics, 60–82.
Yao, S., Kang, Q., Zhou, M., Rawa, M. J., & Abusorrah, A. (2023). A survey of transfer learning for machinery diagnostics and prognostics. Artificial Intelligence Review, 56(4), 2871–2922.
Yucesan, Y. A., Dourado, A., & Viana, F. A. (2021). A survey of modeling for prognosis and health management of industrial equipment. Advanced Engineering Informatics, 50, 101404.
Zhang, G., Zhou, T., & Cai, Y. (2023). Coral-based domain adaptation algorithm for improving the applicability of machine learning models in detecting motor bearing failures. Journal of Computational Methods in Engineering Applications, 1–17.
Zhang, S., Lei, S., Jiefei, G., Ke, L., Lang, Z., & Pecht, M. (2023). Rotating machinery fault detection and diagnosis based on deep domain adaptation: A survey. Chinese Journal of Aeronautics, 36(1), 45–74.
Zhang, Y., Ji, J., Ren, Z., Ni, Q., Gu, F., Feng, K., . . . Liu, Z. (2023). Digital twin-driven partial domain adaptation network for intelligent fault diagnosis of rolling bearing. Reliability Engineering & System Safety, 234, 109186.
Zhu, Y., Pei, Y., Wang, A., Xie, B., & Qian, Z. (2023). A partial domain adaptation scheme based on weighted adversarial nets with improved cbam for fault diagnosis of wind turbine gearbox. Engineering Applications of Artificial Intelligence, 125, 106674.
Zhu, Z., Chen, G., & Tang, G. (2023). Domain adaptation with multi-adversarial learning for open-set cross-domain intelligent bearing fault diagnosis. IEEE Transactions on Instrumentation and Measurement, 72, 1–11.
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.