Remaining Useful Life Prediction Using Attention-LSTM Neural Network of Aircraft Engines
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Published
Jul 4, 2025
Marouane Dida
Abdelhakim Cheriet
Mourad Belhadj
Abstract
Accurate prediction of the Remaining Useful Life (RUL) is essential for the effective implementation of Prognostics and Health Management (PHM) in aerospace, particularly in enhancing aero-engine reliability and forecasting potential failures to reduce maintenance costs and human-related risks.
The NASA Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset, utilized in the 2021 PHM Data Challenge, serves as a widely recognized open-source benchmark, providing simulated turbofan engine data collected under realistic flight conditions. Previous deep learning approaches have leveraged this dataset to predict the remaining useful life of engine units.
However, data-driven methods for RUL prediction in aerospace often encounter challenges such as high model complexity, limited prediction accuracy, and reduced interpretability. To address these issues, this paper presents a novel hybrid framework that incorporates an attention mechanism to enhance aircraft engine RUL prognostics. Specifically, we employ a self-attention mechanism to effectively capture relationships and interactions among different features, enabling the transformation of high-dimensional feature spaces into lower-dimensional representations.
The proposed model, which integrates an LSTM network, demonstrates superior performance in predicting turbofan engine RUL. Experimental results validate its effectiveness, achieving RMSE values of 12.33 and 11.76, along with score values of 200 and 212 on the FD001 and FD003 sub-datasets, respectively. These results surpass those of other state-of-the-art methods on the C-MAPSS dataset.
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Keywords
Remaining useful life (RUL), Attention mechanism, Long short-term memory (LSTM), Deep learning, Prognostics and Health Management (PHM)
References
An, D., Kim, N. H., & Choi, J.-H. (2015). Practical options for selecting data-driven or physics-based prognostics algorithms with reviews. Reliability Engineering & System Safety, 133, 223–236.
Benkedjouh, T., Medjaher, K., Zerhouni, N., & Rechak, S. (2013). Remaining useful life estimation based on nonlinear feature reduction and support vector regression. Engineering Applications of Artificial Intelligence, 26(7), 1751–1760.
Chao, M. A., Kulkarni, C., Goebel, K., & Fink, O. (2022). Fusing physics-based and deep learning models for prognostics. Reliability Engineering & System Safety, 217, 107961.
Chemali, E., Kollmeyer, P. J., Preindl, M., Ahmed, R., & Emadi, A. (2017). Long short-term memory networks for accurate state-of-charge estimation of li-ion batteries. IEEE Transactions on Industrial Electronics, 65(8), 6730–6739.
Chen, C., Shi, J., Lu, N., Zhu, Z. H., & Jiang, B. (2022). Data-driven predictive maintenance strategy considering the uncertainty in remaining useful life prediction. Neurocomputing, 494, 79–88.
Chen, L., Zhang, H., Xiao, J., Nie, L., Shao, J., Liu, W., & Chua, T.-S. (2017). Sca-cnn: Spatial and channel-wise attention in convolutional networks for image captioning. In Proceedings of the ieee conference on computer vision and pattern recognition (pp. 5659–5667).
Chen, X., Shen, Z., He, Z., Sun, C., & Liu, Z. (2013). Remaining life prognostics of rolling bearing based on relative features and multivariable support vector machine. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 227(12), 2849–2860.
Elforjani, M., & Shanbr, S. (2017). Prognosis of bearing acoustic emission signals using supervised machine learning. IEEE Transactions on industrial electronics, 65(7), 5864–5871.
Ferreira, C., & Gonc¸alves, 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.
Han, X., Wang, Z., Xie, M., He, Y., Li, Y., & Wang, W. (2021). Remaining useful life prediction and predictive maintenance strategies for multi-state manufacturing systems considering functional dependence. Reliability Engineering & System Safety, 210, 107560.
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735–1780.
Hu, K., Cheng, Y., Wu, J., Zhu, H., & Shao, X. (2021). Deep bidirectional recurrent neural networks ensemble for remaining useful life prediction of aircraft engine. IEEE Transactions on Cybernetics, 53(4), 2531–2543.
Huang, R., Liao, Y., Zhang, S., & Li, W. (2018). Deep decoupling convolutional neural network for intelligent compound fault diagnosis. Ieee Access, 7, 1848–1858.
Kurz, R., & Brun, K. (2001). Degradation in gas turbine systems. J. Eng. Gas Turbines Power, 123(1), 70–77.
Lee, J., Ardakani, H. D., Yang, S., & Bagheri, B. (2015). Industrial big data analytics and cyber-physical systems for future maintenance & service innovation. Procedia cirp, 38, 3–7.
Lei, Y., Li, N., Guo, L., Li, N., Yan, T., & Lin, J. (2018). Machinery health prognostics: A systematic review from data acquisition to rul prediction. Mechanical systems and signal processing, 104, 799–834.
Li, J., Jia, Y., Niu, M., Zhu, W., & Meng, F. (2023). Remaining useful life prediction of turbofan engines using cnn-lstm-sam approach. IEEE Sensors Journal, 23(9), 10241–10251.
Li, X., Ding, Q., & Sun, J.-Q. (2018). Remaining useful life estimation in prognostics using deep convolution neural networks. Reliability Engineering & System Safety, 172, 1–11.
Liao, X., Chen, S., Wen, P., & Zhao, S. (2023). Remaining useful life with self-attention assisted physics-informed neural network. Advanced Engineering Informatics, 58, 102195.
Lin, L., Wu, J., Fu, S., Zhang, S., Tong, C., & Zu, L. (2024). Channel attention & temporal attention based temporal convolutional network: A dual attention framework for remaining useful life prediction of the aircraft engines. Advanced Engineering Informatics, 60, 102372.
Liu, C., Zhang, L., Yao, R., &Wu, C. (2021). Dual attention-based temporal convolutional network for fault prognosis under time-varying operating conditions. IEEE Transactions on Instrumentation and Measurement, 70, 1–10.
Liu, H., Liu, Z., Jia, W., & Lin, X. (2020). Remaining useful life prediction using a novel feature-attention-based end-to-end approach. IEEE Transactions on Industrial Informatics, 17(2), 1197–1207.
Liu, Z., Liu, H., Jia,W., Zhang, D., & Tan, J. (2021). A multihead neural network with unsymmetrical constraints for remaining useful life prediction. Advanced Engineering Informatics, 50, 101396.
Marouane, D., Belhadj, M., & Cheriet, A. (2024). Prediction of the remaining useful lifetime of turbofan aircraft engines using machine learning: A comparative study. In 2024 4th international conference on embedded & distributed systems (edis) (pp. 137–142).
Miao, H., Li, B., Sun, C., & Liu, J. (2019). Joint learning of degradation assessment and rul prediction for aeroengines via dual-task deep lstm networks. IEEE Transactions on Industrial Informatics, 15(9), 5023–5032.
Peng, K., Jiao, R., Dong, J., & Pi, Y. (2019). A deep belief network based health indicator construction and remaining useful life prediction using improved particle filter. Neurocomputing, 361, 19–28.
Raffel, C., & Ellis, D. P. (2015). Feed-forward networks with attention can solve some long-term memory problems. arXiv preprint arXiv:1512.08756.
Ran, X., Shan, Z., Fang, Y., & Lin, C. (2019). An lstmbased method with attention mechanism for travel time prediction. Sensors, 19(4), 861.
Rohani Bastami, A., Aasi, A., & Arghand, H. A. (2019). Estimation of remaining useful life of rolling element bearings using wavelet packet decomposition and artificial neural network. Iranian Journal of Science and Technology, Transactions of Electrical Engineering, 43, 233–245.
Sateesh Babu, G., Zhao, P., & Li, X.-L. (2016). Deep convolutional neural network based regression approach for estimation of remaining useful life. In Database systems for advanced applications: 21st international conference, dasfaa 2016, dallas, tx, usa, april 16-19, 2016, proceedings, part i 21 (pp. 214–228).
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).
Shah, S. R. B., Chadha, G. S., Schwung, A., & Ding, S. X. (2021). A sequence-to-sequence approach for remaining useful lifetime estimation using attention-augmented bidirectional lstm. Intelligent Systems with Applications, 10, 200049.
Song, H., Liu, X., & Song, M. (2023). Comparative study of data-driven and model-driven approaches in prediction of nuclear power plants operating parameters. Applied Energy, 341, 121077.
Song, S., Lan, C., Xing, J., Zeng, W., & Liu, J. (2017, Feb.). An end-to-end spatio-temporal attention model for human action recognition from skeleton data. Proceedings of the AAAI Conference on Artificial Intelligence, 31(1).
Song, T., Liu, C., Wu, R., Jin, Y., & Jiang, D. (2022). A hierarchical scheme for remaining useful life prediction with long short-term memory networks. Neurocomputing, 487, 22–33.
Sun, C., Ma, M., Zhao, Z., & Chen, X. (2018). Sparse deep stacking network for fault diagnosis of motor. IEEE Transactions on Industrial Informatics, 14(7), 3261–3270.
Tao, F., Qi, Q., Liu, A., & Kusiak, A. (2018). Data-driven smart manufacturing. Journal of Manufacturing Systems, 48, 157–169.
Tian, H., Yang, L., & Ju, B. (2023). Spatial correlation and temporal attention-based lstm for remaining useful life prediction of turbofan engine. Measurement, 214, 112816.
Vaswani, A. (2017). Attention is all you need. Advances in Neural Information Processing Systems.
Wu, L., Wang, Y., Li, X., & Gao, J. (2018). Deep attention-based spatially recursive networks for fine-grained visual recognition. IEEE transactions on cybernetics, 49(5), 1791–1802.
Xia, T., Song, Y., Zheng, Y., Pan, E., & Xi, L. (2020). An ensemble framework based on convolutional bidirectional lstm with multiple time windows for remaining useful life estimation. Computers in Industry, 115, 103182.
Zhai, M., Xiang, X., Zhang, R., Lv, N., & El Saddik, A. (2019). Optical flow estimation using channel attention mechanism and dilated convolutional neural networks. Neurocomputing, 368, 124–132.
Zhang, C., Lim, P., Qin, A. K., & Tan, K. C. (2016). Multiobjective deep belief networks ensemble for remaining useful life estimation in prognostics. IEEE transactions on neural networks and learning systems, 28(10), 2306–2318.
Zhang, J., Jiang, Y.,Wu, S., Li, X., Luo, H., & Yin, S. (2022). Prediction of remaining useful life based on bidirectional gated recurrent unit with temporal self-attention mechanism. Reliability Engineering & System Safety, 221, 108297.
Zhu, J., Chen, N., & Peng, W. (2018). Estimation of bearing remaining useful life based on multiscale convolutional neural network. IEEE Transactions on Industrial Electronics, 66(4), 3208–3216.
Benkedjouh, T., Medjaher, K., Zerhouni, N., & Rechak, S. (2013). Remaining useful life estimation based on nonlinear feature reduction and support vector regression. Engineering Applications of Artificial Intelligence, 26(7), 1751–1760.
Chao, M. A., Kulkarni, C., Goebel, K., & Fink, O. (2022). Fusing physics-based and deep learning models for prognostics. Reliability Engineering & System Safety, 217, 107961.
Chemali, E., Kollmeyer, P. J., Preindl, M., Ahmed, R., & Emadi, A. (2017). Long short-term memory networks for accurate state-of-charge estimation of li-ion batteries. IEEE Transactions on Industrial Electronics, 65(8), 6730–6739.
Chen, C., Shi, J., Lu, N., Zhu, Z. H., & Jiang, B. (2022). Data-driven predictive maintenance strategy considering the uncertainty in remaining useful life prediction. Neurocomputing, 494, 79–88.
Chen, L., Zhang, H., Xiao, J., Nie, L., Shao, J., Liu, W., & Chua, T.-S. (2017). Sca-cnn: Spatial and channel-wise attention in convolutional networks for image captioning. In Proceedings of the ieee conference on computer vision and pattern recognition (pp. 5659–5667).
Chen, X., Shen, Z., He, Z., Sun, C., & Liu, Z. (2013). Remaining life prognostics of rolling bearing based on relative features and multivariable support vector machine. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 227(12), 2849–2860.
Elforjani, M., & Shanbr, S. (2017). Prognosis of bearing acoustic emission signals using supervised machine learning. IEEE Transactions on industrial electronics, 65(7), 5864–5871.
Ferreira, C., & Gonc¸alves, 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.
Han, X., Wang, Z., Xie, M., He, Y., Li, Y., & Wang, W. (2021). Remaining useful life prediction and predictive maintenance strategies for multi-state manufacturing systems considering functional dependence. Reliability Engineering & System Safety, 210, 107560.
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735–1780.
Hu, K., Cheng, Y., Wu, J., Zhu, H., & Shao, X. (2021). Deep bidirectional recurrent neural networks ensemble for remaining useful life prediction of aircraft engine. IEEE Transactions on Cybernetics, 53(4), 2531–2543.
Huang, R., Liao, Y., Zhang, S., & Li, W. (2018). Deep decoupling convolutional neural network for intelligent compound fault diagnosis. Ieee Access, 7, 1848–1858.
Kurz, R., & Brun, K. (2001). Degradation in gas turbine systems. J. Eng. Gas Turbines Power, 123(1), 70–77.
Lee, J., Ardakani, H. D., Yang, S., & Bagheri, B. (2015). Industrial big data analytics and cyber-physical systems for future maintenance & service innovation. Procedia cirp, 38, 3–7.
Lei, Y., Li, N., Guo, L., Li, N., Yan, T., & Lin, J. (2018). Machinery health prognostics: A systematic review from data acquisition to rul prediction. Mechanical systems and signal processing, 104, 799–834.
Li, J., Jia, Y., Niu, M., Zhu, W., & Meng, F. (2023). Remaining useful life prediction of turbofan engines using cnn-lstm-sam approach. IEEE Sensors Journal, 23(9), 10241–10251.
Li, X., Ding, Q., & Sun, J.-Q. (2018). Remaining useful life estimation in prognostics using deep convolution neural networks. Reliability Engineering & System Safety, 172, 1–11.
Liao, X., Chen, S., Wen, P., & Zhao, S. (2023). Remaining useful life with self-attention assisted physics-informed neural network. Advanced Engineering Informatics, 58, 102195.
Lin, L., Wu, J., Fu, S., Zhang, S., Tong, C., & Zu, L. (2024). Channel attention & temporal attention based temporal convolutional network: A dual attention framework for remaining useful life prediction of the aircraft engines. Advanced Engineering Informatics, 60, 102372.
Liu, C., Zhang, L., Yao, R., &Wu, C. (2021). Dual attention-based temporal convolutional network for fault prognosis under time-varying operating conditions. IEEE Transactions on Instrumentation and Measurement, 70, 1–10.
Liu, H., Liu, Z., Jia, W., & Lin, X. (2020). Remaining useful life prediction using a novel feature-attention-based end-to-end approach. IEEE Transactions on Industrial Informatics, 17(2), 1197–1207.
Liu, Z., Liu, H., Jia,W., Zhang, D., & Tan, J. (2021). A multihead neural network with unsymmetrical constraints for remaining useful life prediction. Advanced Engineering Informatics, 50, 101396.
Marouane, D., Belhadj, M., & Cheriet, A. (2024). Prediction of the remaining useful lifetime of turbofan aircraft engines using machine learning: A comparative study. In 2024 4th international conference on embedded & distributed systems (edis) (pp. 137–142).
Miao, H., Li, B., Sun, C., & Liu, J. (2019). Joint learning of degradation assessment and rul prediction for aeroengines via dual-task deep lstm networks. IEEE Transactions on Industrial Informatics, 15(9), 5023–5032.
Peng, K., Jiao, R., Dong, J., & Pi, Y. (2019). A deep belief network based health indicator construction and remaining useful life prediction using improved particle filter. Neurocomputing, 361, 19–28.
Raffel, C., & Ellis, D. P. (2015). Feed-forward networks with attention can solve some long-term memory problems. arXiv preprint arXiv:1512.08756.
Ran, X., Shan, Z., Fang, Y., & Lin, C. (2019). An lstmbased method with attention mechanism for travel time prediction. Sensors, 19(4), 861.
Rohani Bastami, A., Aasi, A., & Arghand, H. A. (2019). Estimation of remaining useful life of rolling element bearings using wavelet packet decomposition and artificial neural network. Iranian Journal of Science and Technology, Transactions of Electrical Engineering, 43, 233–245.
Sateesh Babu, G., Zhao, P., & Li, X.-L. (2016). Deep convolutional neural network based regression approach for estimation of remaining useful life. In Database systems for advanced applications: 21st international conference, dasfaa 2016, dallas, tx, usa, april 16-19, 2016, proceedings, part i 21 (pp. 214–228).
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).
Shah, S. R. B., Chadha, G. S., Schwung, A., & Ding, S. X. (2021). A sequence-to-sequence approach for remaining useful lifetime estimation using attention-augmented bidirectional lstm. Intelligent Systems with Applications, 10, 200049.
Song, H., Liu, X., & Song, M. (2023). Comparative study of data-driven and model-driven approaches in prediction of nuclear power plants operating parameters. Applied Energy, 341, 121077.
Song, S., Lan, C., Xing, J., Zeng, W., & Liu, J. (2017, Feb.). An end-to-end spatio-temporal attention model for human action recognition from skeleton data. Proceedings of the AAAI Conference on Artificial Intelligence, 31(1).
Song, T., Liu, C., Wu, R., Jin, Y., & Jiang, D. (2022). A hierarchical scheme for remaining useful life prediction with long short-term memory networks. Neurocomputing, 487, 22–33.
Sun, C., Ma, M., Zhao, Z., & Chen, X. (2018). Sparse deep stacking network for fault diagnosis of motor. IEEE Transactions on Industrial Informatics, 14(7), 3261–3270.
Tao, F., Qi, Q., Liu, A., & Kusiak, A. (2018). Data-driven smart manufacturing. Journal of Manufacturing Systems, 48, 157–169.
Tian, H., Yang, L., & Ju, B. (2023). Spatial correlation and temporal attention-based lstm for remaining useful life prediction of turbofan engine. Measurement, 214, 112816.
Vaswani, A. (2017). Attention is all you need. Advances in Neural Information Processing Systems.
Wu, L., Wang, Y., Li, X., & Gao, J. (2018). Deep attention-based spatially recursive networks for fine-grained visual recognition. IEEE transactions on cybernetics, 49(5), 1791–1802.
Xia, T., Song, Y., Zheng, Y., Pan, E., & Xi, L. (2020). An ensemble framework based on convolutional bidirectional lstm with multiple time windows for remaining useful life estimation. Computers in Industry, 115, 103182.
Zhai, M., Xiang, X., Zhang, R., Lv, N., & El Saddik, A. (2019). Optical flow estimation using channel attention mechanism and dilated convolutional neural networks. Neurocomputing, 368, 124–132.
Zhang, C., Lim, P., Qin, A. K., & Tan, K. C. (2016). Multiobjective deep belief networks ensemble for remaining useful life estimation in prognostics. IEEE transactions on neural networks and learning systems, 28(10), 2306–2318.
Zhang, J., Jiang, Y.,Wu, S., Li, X., Luo, H., & Yin, S. (2022). Prediction of remaining useful life based on bidirectional gated recurrent unit with temporal self-attention mechanism. Reliability Engineering & System Safety, 221, 108297.
Zhu, J., Chen, N., & Peng, W. (2018). Estimation of bearing remaining useful life based on multiscale convolutional neural network. IEEE Transactions on Industrial Electronics, 66(4), 3208–3216.
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Technical Papers