Fault-Type-Aware Remaining Useful Life Prediction of Aircraft Engines Using an Integrated Deep Learning Framework
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Published
Sep 16, 2025
Junwon Seo
Abstract
Accurate Remaining Useful Life (RUL) prediction is essential for reducing maintenance costs and improving operational efficiency in high-value, complex systems such as aircraft engines. Data-driven approaches have emerged as a primary methodology in RUL estimation research, demonstrating significant improvements in performance. However, discrepancies in degradation trajectories across multiple failure modes can adversely affect the prediction accuracy. To address this challenge, this study proposes an integrated framework based on TS K-Means–BiLSTM to perform RUL prediction considering different failure modes. Specifically, Time Series K-Means Clustering (TS K-Means) is used to cluster time series data into latent failure-mode groups, and a Bidirectional Long Short-Term Memory (BiLSTM) network is subsequently employed to predict the RUL for each group. The proposed framework is validated using the Commercial Modular Aero-Propulsion System Simulation dataset provided by NASA. Experimental results show that the proposed model outperforms existing methods. In addition, it achieves better results than the comparison Bi-LSTM model trained under the same conditions but without fault-type separation. This improvement likely results from minimizing interference between degradation patterns, allowing the model to better distinguish the unique behaviors associated with each fault type. Consequently, the proposed approach demonstrates strong potential for practical RUL prediction tasks.
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Keywords
PHM, predictive maintenance, RUL prediction, aircraft engine, C-MAPSS, deep learning
References
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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. doi:10.1016/j.ress.2022.108330
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Wang, J., Wen, G., Yang, S., & Liu, Y. (2018, October). Remaining useful life estimation in prognostics using deep bidirectional LSTM neural network. In 2018 Prognostics and System Health Management Conference (PHM-Chongqing) (pp. 1037–1042), October 26–28, Chongqing, China. IEEE. doi:10.1109/PHM-Chongqing.2018.00184
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Wu, Y., Yuan, M., Dong, S., Lin, L., & Liu, Y. (2018). Remaining useful life estimation of engineered systems using vanilla LSTM neural networks. Neurocomputing, 275, 167–179. doi:10.1016/j.neucom.2017.05.063
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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. doi:10.1016/j.ress.2021.108297
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Al-Khazraji, H., Nasser, A. R., Hasan, A. M., Al Mhdawi, A. K., Al-Raweshidy, H., & Humaidi, A. J. (2022). Aircraft engines remaining useful life prediction based on a hybrid model of autoencoder and deep belief network. IEEE Access, 10, 82156–82163. doi:10.1109/ACCESS.2022.3188681
Alomari, Y., Andó, M., & Baptista, M. L. (2023). Advancing aircraft engine RUL predictions: An interpretable integrated approach of feature engineering and aggregated feature importance. Scientific Reports, 13, 13466. doi:10.1038/s41598-023-40315-1
Asif, O., Haider, S. A., Naqvi, S. R., Zaki, J. F. W., Kwak, K.-S., & Islam, S. M. R. (2022). A deep learning model for remaining useful life prediction of aircraft turbofan engine on C-MAPSS dataset. IEEE Access, 10, 95425–95440. doi:10.1109/ACCESS.2022.3203406
Berghout, T., Mouss, L.-H., Kadri, O., Saïdi, L., & Benbouzid, M. (2020). Aircraft engines remaining useful life prediction with an improved online sequential extreme learning machine. Applied Sciences, 10(3), 1062. doi:10.3390/app10031062
Bolander, N., Qiu, H., Eklund, N., Hindle, E., & Rosenfeld, T. (2009). Physics-based remaining useful life prediction for aircraft engine bearing prognosis. Annual Conference of the PHM Society, 1(1). doi:10.36001/phmconf.2009.v1i1.1631
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. doi:10.1016/j.ress.2021.107961
Chen, Z., Wu, M., Zhao, R., Guretno, F., Yan, R., & Li, X. (2020). Machine remaining useful life prediction via an attention-based deep learning approach. IEEE Transactions on Industrial Electronics, 68(3), 2521–2531. doi:10.1109/TIE.2020.2972443
Cheng, Y., Hu, K., Wu, J., Zhu, H., & Shao, X. (2021). Autoencoder quasi-recurrent neural networks for remaining useful life prediction of engineering systems. IEEE/ASME Transactions on Mechatronics, 27(2), 1081–1092. doi:10.1109/TMECH.2021.3079729
Cuesta, J., Leturiondo, U., Vidal, Y., & Pozo, F. (2025). A review of prognostics and health management techniques in wind energy. Reliability Engineering & System Safety, 260, 111004. doi:10.1016/j.ress.2025.111004
Cuturi, M., & Blondel, M. (2017, August). Soft-DTW: a differentiable loss function for time-series. In Proceedings of the 34th International Conference on Machine Learning (ICML) (pp. 894–903), August 6–11, Sydney, Australia. PMLR. Retrieved from https://proceedings.mlr.press/v70/cuturi17a.html
Ensarioğlu, K., İnkaya, T., & Emel, E. (2023). Remaining useful life estimation of turbofan engines with deep learning using change-point detection based labeling and feature engineering. Applied Sciences, 13(21), 11893. doi:10.3390/app132111893
Holder, C., Bagnall, A., & Lines, J. (2024). On time series clustering with k-means. arXiv preprint, arXiv:2410.14269. doi:10.48550/arXiv.2410.14269
Hong, C. W., Lee, C., Lee, K., Ko, M.-S., Kim, D. E., & Hur, K. (2020). Remaining useful life prognosis for turbofan engine using explainable deep neural networks with dimensionality reduction. Sensors, 20(22), 6626. doi:10.3390/s20226626
Jayasinghe, L., Samarasinghe, T., Yuen, C., Low, J. C. N., & Ge, S. S. (2019, February). Temporal convolutional memory networks for remaining useful life estimation of industrial machinery. In 2019 IEEE International Conference on Industrial Technology (ICIT) (pp. 915–920). IEEE. doi:10.1109/ICIT.2019.8754956
Keogh, E., & Ratanamahatana, C. A. (2005). Exact indexing of dynamic time warping. Knowledge and Information Systems, 7, 358–386. doi:10.1007/s10115-004-0154-9
Khelif, R., Chebel-Morello, B., Malinowski, S., Laajili, E., Fnaiech, F., & Zerhouni, N. (2017). Direct remaining useful life estimation based on support vector regression. IEEE Transactions on Industrial Electronics, 64(3), 2276–2285. doi:10.1109/TIE.2016.2623260
Li, X., Wang, L., Wang, C., Ma, X., Miao, B., Xu, D., & Cheng, R. (2024). A method for predicting remaining useful life using enhanced Savitzky–Golay filter and improved deep learning framework. Scientific Reports, 14(1), 23983. doi:10.1038/s41598-024-74989-y
Li, X., Zhong, X., Shao, H., Han, T., & Shen, C. (2021). Multi-sensor gearbox fault diagnosis by using feature-fusion covariance matrix and multi-Riemannian kernel ridge regression. Reliability Engineering & System Safety, 216, 108018. doi:10.1016/j.ress.2021.108018
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. doi:10.1016/j.ress.2022.108330
Luo, M. (2006). Data-driven fault detection using trending analysis. Doctoral dissertation, Louisiana State University and Agricultural & Mechanical College, Baton Rouge, LA. Retrieved from https://www.proquest.com/openview/ace8248d483788bddeb4379c7426feee/1?cbl=18750&diss=y&pq-origsite=gscholar
Peng, Z., Wang, Q., Liu, Z., & He, R. (2024). Remaining useful life prediction for aircraft engines under high-pressure compressor degradation faults based on FC-AMSLSTM. Aerospace, 11(4), 293. doi:10.3390/aerospace11040293
Shahapure, K. R., & Nicholas, C. (2020, October). Cluster quality analysis using silhouette score. In 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA) (pp. 747–748), October 6–9, Sydney, Australia. IEEE. doi:10.1109/DSAA49011.2020.00096
Simpson, T., Dervilis, N., & Chatzi, E. (2021). Machine learning approach to model order reduction of nonlinear systems via autoencoder and LSTM networks. Journal of Engineering Mechanics, 147(10), 04021061. doi:10.1061/(ASCE)EM.1943-7889.0001971
Wang, H., Zhang, Z., Li, X., Deng, X., & Jiang, W. (2023). Comprehensive dynamic structure graph neural network for aero-engine remaining useful life prediction. IEEE Transactions on Instrumentation and Measurement, 72, 1–16. doi:10.1109/TIM.2023.3322481
Wang, J., Wen, G., Yang, S., & Liu, Y. (2018, October). Remaining useful life estimation in prognostics using deep bidirectional LSTM neural network. In 2018 Prognostics and System Health Management Conference (PHM-Chongqing) (pp. 1037–1042), October 26–28, Chongqing, China. IEEE. doi:10.1109/PHM-Chongqing.2018.00184
Wang, Y., et al., 2024, "Deep time series models: A comprehensive survey and benchmark" arXiv preprint arXiv:2407.13278
Wen, Z., Fang, Y., Wei, P., Liu, F., Chen, Z., & Wu, M. (2025). Temporal and heterogeneous graph neural network for remaining useful life prediction. IEEE Transactions on Neural Networks and Learning Systems.
Wu, Y., Yuan, M., Dong, S., Lin, L., & Liu, Y. (2018). Remaining useful life estimation of engineered systems using vanilla LSTM neural networks. Neurocomputing, 275, 167–179. doi:10.1016/j.neucom.2017.05.063
Yun, Y., Kim, S., Cho, S., & Choi, J. (2019). Neural network based aircraft engine health management using C-MAPSS data. Journal of Aerospace System Engineering, 13(6), 17–25. doi:10.20910/JASE.2019.13.6.17
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. doi:10.1016/j.ress.2021.108297
Zheng, S., Ristovski, K., Farahat, A., & Gupta, C. (2017, June). Long short-term memory network for remaining useful life estimation. In 2017 IEEE International Conference on Prognostics and Health Management (ICPHM) (pp. 88–95). IEEE. doi:10.1109/ICPHM.2017.7998311
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