Turbofan Engine Remaining Useful Life Prediction based on Physics-aware Hybrid Framework and Fatigue Cycles
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Published
May 18, 2026
Slawomir Szrama
Abstract
Accurate remaining useful life (RUL) prognostics for turbofan engines are critical for advancing aviation safety and optimizing condition-based maintenance. While conventional methods rely on static service intervals or purely data-driven models, this study introduces a physics-aware hybrid framework that synergizes low-cycle fatigue (LCF) dynamics with a multi-architecture neural network. The framework addresses two fundamental limitations in existing approaches: (1) the omission of material fatigue mechanisms in purely data-driven models, and (2) the limited generalizability of simulated training data to real-world operational variability. The proposed model achieves superior degradation tracking by integrating fatigue cycle analytics, derived from engine operational stress profiles, with a hybrid neural architecture comprising Long Short-Term Memory (LSTM) networks for temporal dependencies, Temporal Convolutional Networks (TCN) for local feature extraction, and Multi-Head Self-Attention (MHSA) layers for dynamic weighting of critical sensor inputs. Comprehensive evaluation on both a 12-year real-world fleet dataset (53 F100-PW-229 engines) and the NASA C-MAPSS benchmark demonstrates significant improvements in predictive performance, with RMSE reductions of 29-48% compared to state-of-the-art hybrid models. The results underscore the potential of physics-aware deep learning to revolutionize predictive maintenance practices in aviation.
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Keywords
physics-informed neural networks, fatigue-driven prognostics, operational degradation analytics, multi-modal sensor fusion, turbofan health management, low-cycle fatigue
References
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Cheng, Y., Wang, J., & Li, H. (2020). Remaining useful life prognosis based on ensemble Long Short-Term memory neural network. IEEE Transactions on Instrumentation and Measurement, 70, 1–12. https://doi.org/10.1109/tim.2020.3031113
Deng, K., Liu, X., & Zhang, Y. (2020). A remaining useful life prediction method with long-short term feature processing for aircraft engines. Applied Soft Computing, 93, 106344. https://doi.org/10.1016/j.asoc.2020.106344
Ibrahima, B., & Meriem, H. (2023). Towards Hybrid Predictive Maintenance for Aircraft Engine: Embracing an Ontological-Data Approach. 20th ACS/IEEE International Conference on Computer Systems and Applications (AICCSA), 1–6. https://doi.org/10.1109/aiccsa59173.2023.10479328
Jiang, Y., Lyu, Y., Wang, Y., & Pin, W. (2020). Fusion Network Combined with Bidirectional LSTM Network and Multiscale CNN for Remaining Useful Life Estimation. International Conference on Advanced Computational Intelligence (ICACI), 620–627. https://doi.org/10.1109/ICACI49185.2020.9177775
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Li, B., Zhu, J., & Zhao, X. (2025). A hybrid physics informed predictive scheme for predicting low-cycle fatigue life and reliability of aerospace materials under multiaxial loading conditions. Reliability Engineering & System Safety, 257, 110838. https://doi.org/10.1016/j.ress.2025.110838
Li, M., Zhu, L., Luo, M., & Ke, T. (2025). Remaining useful life prediction of airplane engine based on bidirectional MAMBA and causal discovery. Sensors, 25(11), 3429. https://doi.org/10.3390/s25113429
Li, X., Wang, Y., & Liu, Z. (2018). Predicting Remaining Useful Life of Industrial Equipment Based on Multivariable Monitoring Data Analysis. Proceedings 2018 Chinese Automation Congress, CAC 2018, 1861–1866. https://doi.org/10.1109/cac.2018.8623249
Li, X., Zhang, W., & Chen, J. (2021). An integrated deep multiscale feature fusion network for aeroengine remaining useful life prediction with multisensor data. Knowledge-Based Systems, 235, 107652. https://doi.org/10.1016/j.knosys.2021.107652
Li, Y., Zhao, H., & Sun, L. (2022). Remaining useful life prediction of aero-engine enabled by fusing knowledge and deep learning models. Reliability Engineering & System Safety, 229, 108869. https://doi.org/10.1016/j.ress.2022.108869
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Lodygowski, T., & Szrama, S. (2025). Unsupervised Classification and Remaining Useful Life Prediction for Turbofan Engines Using Autoencoders and Gaussian Mixture Models: A Comprehensive Framework for Predictive Maintenance. Applied Sciences, 15(14), 7884. https://doi.org/10.3390/app15147884
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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. https://doi.org/10.1016/j.ress.2022.108330
Liu, Y., Chen, H., & Wang, X. (2024). A remaining useful life prediction method of mechanical equipment based on Particle Swarm Optimization-Convolutional neural Network-Bidirectional Long Short-Term memory. Machines, 12(5), 342. https://doi.org/10.3390/machines12050342
Ma, J., Zhang, Y., & Li, H. (2018). Predicting the Remaining Useful Life of an Aircraft Engine Using a Stacked Sparse Autoencoder with Multilayer Self-Learning. Complexity, 2018, 1–13. https://doi.org/10.1155/2018/3813029
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Ren, L.-H., Ye, Z.-F., & Zhao, Y.-P. (2022). Long short-term memory neural network with scoring loss function for aero-engine remaining useful life estimation. Proceedings of the Institution of Mechanical Engineers Part G: Journal of Aerospace Engineering, 237(3), 547–560. https://doi.org/10.1177/09544100221103731
Sharma, N. K., & Bojjagani, S. (2024). Mechanical element’s remaining useful life prediction using a hybrid approach of CNN and LSTM. Multimedia Tools and Applications, 83(31), 75927–75953. https://doi.org/10.1007/s11042-024-18546-9
Sharma, R. K. (2024). Framework based on Machine learning approach for prediction of the remaining useful life: A case study of an aviation engine. Journal of Failure Analysis and Prevention, 24(3), 1333–1350. https://doi.org/10.1007/s11668-024-01922-w
Sharma, S., Pandit, A. K., & S, S. (2024). Predicting Aircraft Turbofan Engine Degradation with Recurrent Neural Networks. 2024 IEEE International Conference on Information Technology, Electronics, and Intelligent Communication Systems (ICITEICS), 1–6. https://doi.org/10.1109/iciteics61368.2024.10625502
Szrama, S. (2019). F-16 turbofan engine monitoring system. Silniki Spalinowe/Combustion Engines, 177(2), 23–35. https://doi.org/10.19206/ce-2019-205
Szrama, S. (2024). Turbofan engine health status prediction with neural network pattern recognition and automated feature engineering. Aircraft Engineering and Aerospace Technology, 96(11), 19–26. https://doi.org/10.1108/aeat-04-2024-0111
Szrama, S. (2024b). Turbofan engine health status prediction with artificial neural network. Aviation, 28(4), 225–234. https://doi.org/10.3846/aviation.2024.22554
Szrama, S. (2025). Optimizing aircraft engine longevity: A comparative framework for dynamically adaptive predictive maintenance using autoencoders, LSTMs, and Gaussian processes. Engineering Applications of Artificial Intelligence, 156, 111199. https://doi.org/10.1016/j.engappai.2025.111199
Szrama, S., & Lodygowski, T. (2024). Aircraft Engine Remaining Useful Life Prediction using neural networks and real-life engine operational data. Advances in Engineering Software, 192, 103645. https://doi.org/10.1016/j.advengsoft.2024.103645
Thakkar, U., & Chaoui, H. (2023). Prognostic and health management of an aircraft turbofan engine using machine learning. 2021 IEEE Vehicle Power and Propulsion Conference (VPPC), 1–6. https://doi.org/10.1109/vppc60535.2023.10403231
Van Den Oord, A., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A., & Kavukcuoglu, K. (2016). WaveNet: a generative model for raw audio. arXiv. https://doi.org/10.48550/arxiv.1609.03499
Wang, F., Liu, X., & Zhang, Y. (2023). Remaining useful life prediction of aero-engines based on random-coefficient regression model considering random failure threshold. Journal of Systems Engineering and Electronics, 34(2), 530–542. https://doi.org/10.23919/jsee.2023.000042
Wang, L., Chen, H., & Li, J. (2022). Long Short-Term Memory Neural Network with Transfer Learning and Ensemble Learning for Remaining Useful Life Prediction. Sensors, 22(15), 5744. https://doi.org/10.3390/s22155744
Wang, S., Li, X., & Zhang, Y. (2018). A Remaining Useful Life Prediction Model Based on Hybrid Long-Short Sequences for Engines. IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, 1757–1762. https://doi.org/10.1109/itsc.2018.8569668
Wang, W., Liu, Y., & Chen, Z. (2021). Residual convolution Long Short-Term memory network for machines remaining useful life prediction and uncertainty quantification. Journal of Dynamics Monitoring and Diagnostics, 1(1), 2–8. https://doi.org/10.37965/jdmd.v2i2.43
Wen, L., Gao, L., & Li, X. (2023). A new multi-sensor fusion with hybrid Convolutional Neural Network with Wiener model for remaining useful life estimation. Engineering Applications of Artificial Intelligence, 126, 106934. https://doi.org/10.1016/j.engappai.2023.106934
Xia, T., Liu, Y., & Li, H. (2021). Multiscale similarity ensemble framework for remaining useful life prediction. Measurement, 188, 110565. https://doi.org/10.1016/j.measurement.2021.110565
Zha, W., & Ye, Y. (2024). An aero-engine remaining useful life prediction model based on feature selection and the improved TCN. Franklin Open, 6, 100083. https://doi.org/10.1016/j.fraope.2024.100083
Zhang, H., Li, X., & Wang, Y. (2020). Attention-Based LSTM network for rotatory machine remaining useful life prediction. IEEE Access, 8, 132188–132199. https://doi.org/10.1109/access.2020.3010068
Zhang, J., Jiang, Y., Wu, S., Li, X., & Luo, H. (2022). Prediction of remaining useful life based on bidirectional gated recurrent unit with temporal self-attention mechanism. Reliability Engineering & System Safety, 221, 108297.
Zheng, Y., Wang, L., & Chen, X. (2022). Prediction of remaining useful life using fused deep learning models: a case study of turbofan engines. Journal of Computing and Information Science in Engineering, 22(5). https://doi.org/10.1115/1.4054090
Zhu, Q., Liu, Y., & Wang, H. (2024). Contrastive BILSTM-enabled health representation learning for remaining useful life prediction. Reliability Engineering & System Safety, 249, 110210. https://doi.org/10.1016/j.ress.2024.110210
Cheng, Y., Wang, J., & Li, H. (2020). Remaining useful life prognosis based on ensemble Long Short-Term memory neural network. IEEE Transactions on Instrumentation and Measurement, 70, 1–12. https://doi.org/10.1109/tim.2020.3031113
Deng, K., Liu, X., & Zhang, Y. (2020). A remaining useful life prediction method with long-short term feature processing for aircraft engines. Applied Soft Computing, 93, 106344. https://doi.org/10.1016/j.asoc.2020.106344
Ibrahima, B., & Meriem, H. (2023). Towards Hybrid Predictive Maintenance for Aircraft Engine: Embracing an Ontological-Data Approach. 20th ACS/IEEE International Conference on Computer Systems and Applications (AICCSA), 1–6. https://doi.org/10.1109/aiccsa59173.2023.10479328
Jiang, Y., Lyu, Y., Wang, Y., & Pin, W. (2020). Fusion Network Combined with Bidirectional LSTM Network and Multiscale CNN for Remaining Useful Life Estimation. International Conference on Advanced Computational Intelligence (ICACI), 620–627. https://doi.org/10.1109/ICACI49185.2020.9177775
Lan, G., Li, Q., & Cheng, N. (2018). Remaining useful life estimation of Turbofan engine using LSTM neural networks. 2018 IEEE CSAA Guidance, Navigation and Control Conference (CGNCC), 1–5. https://doi.org/10.1109/gncc42960.2018.9019107
Li, B., Zhu, J., & Zhao, X. (2025). A hybrid physics informed predictive scheme for predicting low-cycle fatigue life and reliability of aerospace materials under multiaxial loading conditions. Reliability Engineering & System Safety, 257, 110838. https://doi.org/10.1016/j.ress.2025.110838
Li, M., Zhu, L., Luo, M., & Ke, T. (2025). Remaining useful life prediction of airplane engine based on bidirectional MAMBA and causal discovery. Sensors, 25(11), 3429. https://doi.org/10.3390/s25113429
Li, X., Wang, Y., & Liu, Z. (2018). Predicting Remaining Useful Life of Industrial Equipment Based on Multivariable Monitoring Data Analysis. Proceedings 2018 Chinese Automation Congress, CAC 2018, 1861–1866. https://doi.org/10.1109/cac.2018.8623249
Li, X., Zhang, W., & Chen, J. (2021). An integrated deep multiscale feature fusion network for aeroengine remaining useful life prediction with multisensor data. Knowledge-Based Systems, 235, 107652. https://doi.org/10.1016/j.knosys.2021.107652
Li, Y., Zhao, H., & Sun, L. (2022). Remaining useful life prediction of aero-engine enabled by fusing knowledge and deep learning models. Reliability Engineering & System Safety, 229, 108869. https://doi.org/10.1016/j.ress.2022.108869
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. https://doi.org/10.1016/j.aei.2023.102195
Lodygowski, T., & Szrama, S. (2025). Unsupervised Classification and Remaining Useful Life Prediction for Turbofan Engines Using Autoencoders and Gaussian Mixture Models: A Comprehensive Framework for Predictive Maintenance. Applied Sciences, 15(14), 7884. https://doi.org/10.3390/app15147884
Long, J., Shelhamer, E., & Darrell, T. (2015). Fully convolutional networks for semantic segmentation. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2015.7298965
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. https://doi.org/10.1016/j.ress.2022.108330
Liu, Y., Chen, H., & Wang, X. (2024). A remaining useful life prediction method of mechanical equipment based on Particle Swarm Optimization-Convolutional neural Network-Bidirectional Long Short-Term memory. Machines, 12(5), 342. https://doi.org/10.3390/machines12050342
Ma, J., Zhang, Y., & Li, H. (2018). Predicting the Remaining Useful Life of an Aircraft Engine Using a Stacked Sparse Autoencoder with Multilayer Self-Learning. Complexity, 2018, 1–13. https://doi.org/10.1155/2018/3813029
Peng, D., Li, X., & Wang, Y. (2020). An SW-ELM based remaining Useful life prognostic approach for aircraft engines. IFAC-PapersOnLine, 53(2), 13601–13606. https://doi.org/10.1016/j.ifacol.2020.12.853
Ren, L., Qin, H., Xie, Z., Li, B., & Xu, K. (2022). Aero-Engine Remaining Useful Life Estimation Based on Multi-Head Networks. IEEE Transactions on Instrumentation & Measurement, 71, 1–10. https://doi.org/10.1109/TIM.2022.3149094
Ren, L.-H., Ye, Z.-F., & Zhao, Y.-P. (2022). Long short-term memory neural network with scoring loss function for aero-engine remaining useful life estimation. Proceedings of the Institution of Mechanical Engineers Part G: Journal of Aerospace Engineering, 237(3), 547–560. https://doi.org/10.1177/09544100221103731
Sharma, N. K., & Bojjagani, S. (2024). Mechanical element’s remaining useful life prediction using a hybrid approach of CNN and LSTM. Multimedia Tools and Applications, 83(31), 75927–75953. https://doi.org/10.1007/s11042-024-18546-9
Sharma, R. K. (2024). Framework based on Machine learning approach for prediction of the remaining useful life: A case study of an aviation engine. Journal of Failure Analysis and Prevention, 24(3), 1333–1350. https://doi.org/10.1007/s11668-024-01922-w
Sharma, S., Pandit, A. K., & S, S. (2024). Predicting Aircraft Turbofan Engine Degradation with Recurrent Neural Networks. 2024 IEEE International Conference on Information Technology, Electronics, and Intelligent Communication Systems (ICITEICS), 1–6. https://doi.org/10.1109/iciteics61368.2024.10625502
Szrama, S. (2019). F-16 turbofan engine monitoring system. Silniki Spalinowe/Combustion Engines, 177(2), 23–35. https://doi.org/10.19206/ce-2019-205
Szrama, S. (2024). Turbofan engine health status prediction with neural network pattern recognition and automated feature engineering. Aircraft Engineering and Aerospace Technology, 96(11), 19–26. https://doi.org/10.1108/aeat-04-2024-0111
Szrama, S. (2024b). Turbofan engine health status prediction with artificial neural network. Aviation, 28(4), 225–234. https://doi.org/10.3846/aviation.2024.22554
Szrama, S. (2025). Optimizing aircraft engine longevity: A comparative framework for dynamically adaptive predictive maintenance using autoencoders, LSTMs, and Gaussian processes. Engineering Applications of Artificial Intelligence, 156, 111199. https://doi.org/10.1016/j.engappai.2025.111199
Szrama, S., & Lodygowski, T. (2024). Aircraft Engine Remaining Useful Life Prediction using neural networks and real-life engine operational data. Advances in Engineering Software, 192, 103645. https://doi.org/10.1016/j.advengsoft.2024.103645
Thakkar, U., & Chaoui, H. (2023). Prognostic and health management of an aircraft turbofan engine using machine learning. 2021 IEEE Vehicle Power and Propulsion Conference (VPPC), 1–6. https://doi.org/10.1109/vppc60535.2023.10403231
Van Den Oord, A., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A., & Kavukcuoglu, K. (2016). WaveNet: a generative model for raw audio. arXiv. https://doi.org/10.48550/arxiv.1609.03499
Wang, F., Liu, X., & Zhang, Y. (2023). Remaining useful life prediction of aero-engines based on random-coefficient regression model considering random failure threshold. Journal of Systems Engineering and Electronics, 34(2), 530–542. https://doi.org/10.23919/jsee.2023.000042
Wang, L., Chen, H., & Li, J. (2022). Long Short-Term Memory Neural Network with Transfer Learning and Ensemble Learning for Remaining Useful Life Prediction. Sensors, 22(15), 5744. https://doi.org/10.3390/s22155744
Wang, S., Li, X., & Zhang, Y. (2018). A Remaining Useful Life Prediction Model Based on Hybrid Long-Short Sequences for Engines. IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, 1757–1762. https://doi.org/10.1109/itsc.2018.8569668
Wang, W., Liu, Y., & Chen, Z. (2021). Residual convolution Long Short-Term memory network for machines remaining useful life prediction and uncertainty quantification. Journal of Dynamics Monitoring and Diagnostics, 1(1), 2–8. https://doi.org/10.37965/jdmd.v2i2.43
Wen, L., Gao, L., & Li, X. (2023). A new multi-sensor fusion with hybrid Convolutional Neural Network with Wiener model for remaining useful life estimation. Engineering Applications of Artificial Intelligence, 126, 106934. https://doi.org/10.1016/j.engappai.2023.106934
Xia, T., Liu, Y., & Li, H. (2021). Multiscale similarity ensemble framework for remaining useful life prediction. Measurement, 188, 110565. https://doi.org/10.1016/j.measurement.2021.110565
Zha, W., & Ye, Y. (2024). An aero-engine remaining useful life prediction model based on feature selection and the improved TCN. Franklin Open, 6, 100083. https://doi.org/10.1016/j.fraope.2024.100083
Zhang, H., Li, X., & Wang, Y. (2020). Attention-Based LSTM network for rotatory machine remaining useful life prediction. IEEE Access, 8, 132188–132199. https://doi.org/10.1109/access.2020.3010068
Zhang, J., Jiang, Y., Wu, S., Li, X., & Luo, H. (2022). Prediction of remaining useful life based on bidirectional gated recurrent unit with temporal self-attention mechanism. Reliability Engineering & System Safety, 221, 108297.
Zheng, Y., Wang, L., & Chen, X. (2022). Prediction of remaining useful life using fused deep learning models: a case study of turbofan engines. Journal of Computing and Information Science in Engineering, 22(5). https://doi.org/10.1115/1.4054090
Zhu, Q., Liu, Y., & Wang, H. (2024). Contrastive BILSTM-enabled health representation learning for remaining useful life prediction. Reliability Engineering & System Safety, 249, 110210. https://doi.org/10.1016/j.ress.2024.110210
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