Remaining Useful Life Prediction across Datasets via Semi-Supervised Domain Adaptation
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Published
Jul 17, 2026
Gengyu Li
Takehisa Yairi
Abstract
Remaining useful life (RUL) prediction has become a critical task, as accurate prediction enables effective maintenance planning and minimizes unplanned downtime. Complex systems frequently operate under diverse operating conditions, with distributional differences and limited RUL labels. To address these issues, domain adaptation (DA) methods have attracted increasing attention. Mainstream RUL prediction DA methods mainly adopt an unsupervised setting (UDA), relying solely on unlabeled target-domain data. However, prediction performances of UDA methods are far inferior to Target-Only (TO) models trained on full labeled target data, and it fails to effectively transfer degradation-related knowledge for RUL prediction when source-target distribution differences are large. Additionally, existing research mainly focuses on in-dataset adaptation across operating conditions, lacking systematic exploration of cross-dataset RUL prediction. Therefore, this work proposes a semi-supervised domain adaptation (SSDA) workflow for RUL prediction between different datasets. We design an alignment pipeline to address feature discrepancies between datasets and sampling frequency differences. During SSDA training, the original RUL labels of the source domain are treated as noisy labels with respect to the target domain and are progressively modified throughout the adaptation process. We conduct two case studies, one on turbofan engines and the other on rolling bearings. The experimental results demonstrate that the proposed approach can effectively realize cross-dataset RUL prediction.
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Keywords
remaining useful life, deep learning, domain adaptation
References
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Cheng, Y., Wang, C., Wu, J., Zhu, H., & Lee, C. K. (2022). Multi-dimensional recurrent neural network for remaining useful life prediction under variable operating conditions and multiple fault modes. Applied Soft Computing, 118, 108507.
Chi, Y., Dong, Y., Wang, Z. J., Yu, F. R., & Leung, V. C. (2022). Knowledge-based fault diagnosis in industrial internet of things: a survey. IEEE Internet of Things Journal, 9(15), 12886–12900.
Cınar, Z. M., Abdussalam Nuhu, A., Zeeshan, Q., Korhan, O., Asmael, M., & Safaei, B. (2020). Machine learning in predictive maintenance towards sustainable smart manufacturing in industry 4.0. Sustainability, 12(19), 8211.
da Costa, P. R. d. O., Akc¸ay, A., Zhang, Y., & Kaymak, U. (2020). Remaining useful lifetime prediction via deep domain adaptation. Reliability Engineering & System Safety, 195, 106682.
Ferreira, C., & Goncalves, G. (2022). Remaining useful life prediction and challenges: A literature review on the use of machine learning methods. Journal of manufacturing systems, 63, 550–562.
Fu, S., Zhang, Y., Lin, L., Zhao, M., & Zhong, S.-s. (2021). Deep residual lstm with domain-invariance for remaining useful life prediction across domains. Reliability Engineering & System Safety, 216, 108012.
Ganin, Y., & Lempitsky, V. (2015). Unsupervised domain adaptation by backpropagation. In International conference on machine learning (pp. 1180–1189).
Gbore, O. O., Shafiq, M., Jain, A. K., & McGlinchey, D. (2025). Towards reliable rul prediction: Impact of feature selection on degradation modelling. International Journal of Prognostics and Health Management, 16(2).
Gebraeel, N., Lei, Y., Li, N., Si, X., Zio, E., et al. (2023). Prognostics and remaining useful life prediction of machinery: advances, opportunities and challenges. Journal of Dynamics, Monitoring and Diagnostics, 1–12.
Ghifary, M., Kleijn, W. B., Zhang, M., Balduzzi, D., & Li, W. (2016). Deep reconstruction-classification networks for unsupervised domain adaptation. In European conference on computer vision (pp. 597–613).
Glorot, X., Bordes, A., & Bengio, Y. (2011). Domain adaptation for large-scale sentiment classification: A deep learning approach. In Proceedings of the 28th international conference on machine learning (icml-11) (pp. 513–520).
Huang, C.-G., Zhu, J., Han, Y., & Peng, W. (2021). A novel bayesian deep dual network with unsupervised domain adaptation for transfer fault prognosis across different machines. IEEE Sensors Journal, 22(8), 7855–7867.
Joseph, M., & Lalwani, V. (2025). Lama-net: Unsupervised domain adaptation via latent alignment and manifold learning for rul prediction. In 2025 5th international conference on ai-ml-systems (aimlsystems) (pp. 8–18).
Lei, Y., Li, N., Gontarz, S., Lin, J., Radkowski, S., & Dybala, J. (2016). A model-based method for remaining useful life prediction of machinery. IEEE Transactions on reliability, 65(3), 1314–1326.
Li, G., & Yairi, T. (2025). Semi-supervised domain adaptation with auxiliary task learning for rul prediction. In 2025 ieee international conference on prognostics and health management (icphm) (pp. 1–8).
Li, X., Zhang, W., Li, X., & Hao, H. (2023). Partial domain adaptation in remaining useful life prediction with incomplete target data. IEEE/ASME Transactions on Mechatronics, 29(3), 1903–1913.
Liu, C., & Gryllias, K. (2020). Unsupervised domain adaptation based remaining useful life prediction of rolling element bearings. In Phm society european conference (Vol. 5, pp. 10–10).
Liu, L., Song, X., & Zhou, Z. (2022). Aircraft engine remaining useful life estimation via a double attention-based data-driven architecture. Reliability Engineering & System Safety, 221, 108330.
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).
Lövberg, A. (2021). Remaining useful life prediction of aircraft engines with variable length input sequences. In Annual conference of the phm society (Vol. 13).
Lu, X., Jiang, Q., Shen, Y., Lin, X., Xu, F., & Zhu, Q. (2024). Enhanced residual convolutional domain adaptation network with cbam for rul prediction of cross-machine rolling bearing. Reliability Engineering & System Safety, 245, 109976.
Luo, Q., Chang, Y., Chen, J., Jing, H., Lv, H., & Pan, T. (2020). Multiple degradation mode analysis via gated recurrent unit mode recognizer and life predictors for complex equipment. Computers in Industry, 123, 103332.
Merkt, O. (2019). On the use of predictive models for improving the quality of industrial maintenance: An analytical literature review of maintenance strategies. In 2019 federated conference on computer science and information systems (fedcsis) (pp. 693–704).
Motiian, S., Piccirilli, M., Adjeroh, D. A., & Doretto, G. (2017). Unified deep supervised domain adaptation and generalization. In Proceedings of the ieee international conference on computer vision (pp. 5715–5725).
Nectoux, P., Gouriveau, R., Medjaher, K., Ramasso, E., Chebel-Morello, B., Zerhouni, N., & Varnier, C. (2012). Pronostia: An experimental platform for bearings accelerated degradation tests. In Ieee international conference on prognostics and health management, phm’12. (pp. 1–8).
Nejjar, I., Geissmann, F., Zhao, M., Taal, C., & Fink, O. (2024). Domain adaptation via alignment of operation profile for remaining useful lifetime prediction. Reliability Engineering & System Safety, 242, 109718.
Pan, B.,Wang,W.,Wen, J., & Li, Y. (2023). Semi-supervised adversarial transfer networks for cross-domain intelligent fault diagnosis of rolling bearings. Applied Sciences, 13(4), 2626.
Pei, Z., Cao, Z., Long, M., & Wang, J. (2018). Multiadversarial domain adaptation. In Proceedings of the aaai conference on artificial intelligence (Vol. 32).
Saito, K., Kim, D., Sclaroff, S., Darrell, T., & Saenko, K. (2019). Semi-supervised domain adaptation via minimax entropy. In Proceedings of the ieee/cvf international conference on computer vision (pp. 8050–8058).
Saxena, A., Goebel, K., Simon, D., & Eklund, N. (2008). Damage propagation modeling for aircraft engine run-to-failure simulation. In 2008 international conference on prognostics and health management (pp. 1–9).
Sun, B., & Saenko, K. (2016). Deep coral: Correlation alignment for deep domain adaptation. In European conference on computer vision (pp. 443–450).
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.
Tzeng, E., Hoffman, J., Saenko, K., & Darrell, T. (2017). Adversarial discriminative domain adaptation. In Proceedings of the ieee conference on computer vision and pattern recognition (pp. 7167–7176).
Tzeng, E., Hoffman, J., Zhang, N., Saenko, K., & Darrell, T. (2014). Deep domain confusion: Maximizing for domain invariance. arXiv preprint arXiv:1412.3474.
Yaguo, L., Tianyu, H., Biao, W., Naipeng, L., Tao, Y., & Jun, Y. (2019). Xjtu-sy rolling element bearing accelerated life test datasets: A tutorial. Journal of Mechanical Engineering, 55(16), 1–6.
Yang, G., & Lang, H. (2022). Semisupervised heterogeneous domain adaptation via dynamic joint correlation alignment network for ship classification in sar imagery. IEEE Geoscience and Remote Sensing Letters, 19, 1–5.
Yao, T., Pan, Y., Ngo, C.-W., Li, H., & Mei, T. (2015). Semisupervised domain adaptation with subspace learning for visual recognition. In Proceedings of the ieee conference on computer vision and pattern recognition (pp. 2142–2150).
Yu, W., Kim, I. Y., & Mechefske, C. (2019). Remaining useful life estimation using a bidirectional recurrent neural network based autoencoder scheme. Mechanical Systems and Signal Processing, 129, 764–780.
Yu, Y.-C., & Lin, H.-T. (2023, June). Semi-supervised domain adaptation with source label adaptation. In Proceedings of the ieee/cvf conference on computer vision and pattern recognition (cvpr) (p. 24100-24109).
Zhao, D., & Liu, F. (2022). Cross-condition and cross-platform remaining useful life estimation via adversarial-based domain adaptation. Scientific Reports, 12(1), 878.
Zheng, S., Ristovski, K., Farahat, A., & Gupta, C. (2017). Long short-term memory network for remaining useful life estimation. In 2017 ieee international conference on prognostics and health management (icphm) (pp. 88–95).
Zhuang, J., Jia, M., Ding, Y., & Ding, P. (2021). Temporal convolution-based transferable cross-domain adaptation approach for remaining useful life estimation under variable failure behaviors. Reliability Engineering & System Safety, 216, 107946.
Zonta, T., Da Costa, C. A., da Rosa Righi, R., De Lima, M. J., Da Trindade, E. S., & Li, G. P. (2020). Predictive maintenance in the industry 4.0: A systematic literature review. Computers & industrial engineering, 150, 106889.
Zou, Y., Yu, Z., Liu, X., Kumar, B., & Wang, J. (2019). Confidence regularized self-training. In Proceedings of the ieee/cvf international conference on computer vision (pp. 5982–5991).
Berghout, T., Mouss, L.-H., Bentrcia, T., & Benbouzid, M. (2021). A semi-supervised deep transfer learning approach for rolling-element bearing remaining useful life prediction. IEEE Transactions on Energy Conversion, 37(2), 1200–1210.
Cheng, Y., Wang, C., Wu, J., Zhu, H., & Lee, C. K. (2022). Multi-dimensional recurrent neural network for remaining useful life prediction under variable operating conditions and multiple fault modes. Applied Soft Computing, 118, 108507.
Chi, Y., Dong, Y., Wang, Z. J., Yu, F. R., & Leung, V. C. (2022). Knowledge-based fault diagnosis in industrial internet of things: a survey. IEEE Internet of Things Journal, 9(15), 12886–12900.
Cınar, Z. M., Abdussalam Nuhu, A., Zeeshan, Q., Korhan, O., Asmael, M., & Safaei, B. (2020). Machine learning in predictive maintenance towards sustainable smart manufacturing in industry 4.0. Sustainability, 12(19), 8211.
da Costa, P. R. d. O., Akc¸ay, A., Zhang, Y., & Kaymak, U. (2020). Remaining useful lifetime prediction via deep domain adaptation. Reliability Engineering & System Safety, 195, 106682.
Ferreira, C., & Goncalves, G. (2022). Remaining useful life prediction and challenges: A literature review on the use of machine learning methods. Journal of manufacturing systems, 63, 550–562.
Fu, S., Zhang, Y., Lin, L., Zhao, M., & Zhong, S.-s. (2021). Deep residual lstm with domain-invariance for remaining useful life prediction across domains. Reliability Engineering & System Safety, 216, 108012.
Ganin, Y., & Lempitsky, V. (2015). Unsupervised domain adaptation by backpropagation. In International conference on machine learning (pp. 1180–1189).
Gbore, O. O., Shafiq, M., Jain, A. K., & McGlinchey, D. (2025). Towards reliable rul prediction: Impact of feature selection on degradation modelling. International Journal of Prognostics and Health Management, 16(2).
Gebraeel, N., Lei, Y., Li, N., Si, X., Zio, E., et al. (2023). Prognostics and remaining useful life prediction of machinery: advances, opportunities and challenges. Journal of Dynamics, Monitoring and Diagnostics, 1–12.
Ghifary, M., Kleijn, W. B., Zhang, M., Balduzzi, D., & Li, W. (2016). Deep reconstruction-classification networks for unsupervised domain adaptation. In European conference on computer vision (pp. 597–613).
Glorot, X., Bordes, A., & Bengio, Y. (2011). Domain adaptation for large-scale sentiment classification: A deep learning approach. In Proceedings of the 28th international conference on machine learning (icml-11) (pp. 513–520).
Huang, C.-G., Zhu, J., Han, Y., & Peng, W. (2021). A novel bayesian deep dual network with unsupervised domain adaptation for transfer fault prognosis across different machines. IEEE Sensors Journal, 22(8), 7855–7867.
Joseph, M., & Lalwani, V. (2025). Lama-net: Unsupervised domain adaptation via latent alignment and manifold learning for rul prediction. In 2025 5th international conference on ai-ml-systems (aimlsystems) (pp. 8–18).
Lei, Y., Li, N., Gontarz, S., Lin, J., Radkowski, S., & Dybala, J. (2016). A model-based method for remaining useful life prediction of machinery. IEEE Transactions on reliability, 65(3), 1314–1326.
Li, G., & Yairi, T. (2025). Semi-supervised domain adaptation with auxiliary task learning for rul prediction. In 2025 ieee international conference on prognostics and health management (icphm) (pp. 1–8).
Li, X., Zhang, W., Li, X., & Hao, H. (2023). Partial domain adaptation in remaining useful life prediction with incomplete target data. IEEE/ASME Transactions on Mechatronics, 29(3), 1903–1913.
Liu, C., & Gryllias, K. (2020). Unsupervised domain adaptation based remaining useful life prediction of rolling element bearings. In Phm society european conference (Vol. 5, pp. 10–10).
Liu, L., Song, X., & Zhou, Z. (2022). Aircraft engine remaining useful life estimation via a double attention-based data-driven architecture. Reliability Engineering & System Safety, 221, 108330.
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).
Lövberg, A. (2021). Remaining useful life prediction of aircraft engines with variable length input sequences. In Annual conference of the phm society (Vol. 13).
Lu, X., Jiang, Q., Shen, Y., Lin, X., Xu, F., & Zhu, Q. (2024). Enhanced residual convolutional domain adaptation network with cbam for rul prediction of cross-machine rolling bearing. Reliability Engineering & System Safety, 245, 109976.
Luo, Q., Chang, Y., Chen, J., Jing, H., Lv, H., & Pan, T. (2020). Multiple degradation mode analysis via gated recurrent unit mode recognizer and life predictors for complex equipment. Computers in Industry, 123, 103332.
Merkt, O. (2019). On the use of predictive models for improving the quality of industrial maintenance: An analytical literature review of maintenance strategies. In 2019 federated conference on computer science and information systems (fedcsis) (pp. 693–704).
Motiian, S., Piccirilli, M., Adjeroh, D. A., & Doretto, G. (2017). Unified deep supervised domain adaptation and generalization. In Proceedings of the ieee international conference on computer vision (pp. 5715–5725).
Nectoux, P., Gouriveau, R., Medjaher, K., Ramasso, E., Chebel-Morello, B., Zerhouni, N., & Varnier, C. (2012). Pronostia: An experimental platform for bearings accelerated degradation tests. In Ieee international conference on prognostics and health management, phm’12. (pp. 1–8).
Nejjar, I., Geissmann, F., Zhao, M., Taal, C., & Fink, O. (2024). Domain adaptation via alignment of operation profile for remaining useful lifetime prediction. Reliability Engineering & System Safety, 242, 109718.
Pan, B.,Wang,W.,Wen, J., & Li, Y. (2023). Semi-supervised adversarial transfer networks for cross-domain intelligent fault diagnosis of rolling bearings. Applied Sciences, 13(4), 2626.
Pei, Z., Cao, Z., Long, M., & Wang, J. (2018). Multiadversarial domain adaptation. In Proceedings of the aaai conference on artificial intelligence (Vol. 32).
Saito, K., Kim, D., Sclaroff, S., Darrell, T., & Saenko, K. (2019). Semi-supervised domain adaptation via minimax entropy. In Proceedings of the ieee/cvf international conference on computer vision (pp. 8050–8058).
Saxena, A., Goebel, K., Simon, D., & Eklund, N. (2008). Damage propagation modeling for aircraft engine run-to-failure simulation. In 2008 international conference on prognostics and health management (pp. 1–9).
Sun, B., & Saenko, K. (2016). Deep coral: Correlation alignment for deep domain adaptation. In European conference on computer vision (pp. 443–450).
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.
Tzeng, E., Hoffman, J., Saenko, K., & Darrell, T. (2017). Adversarial discriminative domain adaptation. In Proceedings of the ieee conference on computer vision and pattern recognition (pp. 7167–7176).
Tzeng, E., Hoffman, J., Zhang, N., Saenko, K., & Darrell, T. (2014). Deep domain confusion: Maximizing for domain invariance. arXiv preprint arXiv:1412.3474.
Yaguo, L., Tianyu, H., Biao, W., Naipeng, L., Tao, Y., & Jun, Y. (2019). Xjtu-sy rolling element bearing accelerated life test datasets: A tutorial. Journal of Mechanical Engineering, 55(16), 1–6.
Yang, G., & Lang, H. (2022). Semisupervised heterogeneous domain adaptation via dynamic joint correlation alignment network for ship classification in sar imagery. IEEE Geoscience and Remote Sensing Letters, 19, 1–5.
Yao, T., Pan, Y., Ngo, C.-W., Li, H., & Mei, T. (2015). Semisupervised domain adaptation with subspace learning for visual recognition. In Proceedings of the ieee conference on computer vision and pattern recognition (pp. 2142–2150).
Yu, W., Kim, I. Y., & Mechefske, C. (2019). Remaining useful life estimation using a bidirectional recurrent neural network based autoencoder scheme. Mechanical Systems and Signal Processing, 129, 764–780.
Yu, Y.-C., & Lin, H.-T. (2023, June). Semi-supervised domain adaptation with source label adaptation. In Proceedings of the ieee/cvf conference on computer vision and pattern recognition (cvpr) (p. 24100-24109).
Zhao, D., & Liu, F. (2022). Cross-condition and cross-platform remaining useful life estimation via adversarial-based domain adaptation. Scientific Reports, 12(1), 878.
Zheng, S., Ristovski, K., Farahat, A., & Gupta, C. (2017). Long short-term memory network for remaining useful life estimation. In 2017 ieee international conference on prognostics and health management (icphm) (pp. 88–95).
Zhuang, J., Jia, M., Ding, Y., & Ding, P. (2021). Temporal convolution-based transferable cross-domain adaptation approach for remaining useful life estimation under variable failure behaviors. Reliability Engineering & System Safety, 216, 107946.
Zonta, T., Da Costa, C. A., da Rosa Righi, R., De Lima, M. J., Da Trindade, E. S., & Li, G. P. (2020). Predictive maintenance in the industry 4.0: A systematic literature review. Computers & industrial engineering, 150, 106889.
Zou, Y., Yu, Z., Liu, X., Kumar, B., & Wang, J. (2019). Confidence regularized self-training. In Proceedings of the ieee/cvf international conference on computer vision (pp. 5982–5991).
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