Remaining Useful Life Estimation for Aircraft Engines with Risk-Aware Prediction Intervals via Conformalized Quantile Regression
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Published
Apr 27, 2026
Colby Robinson
https://orcid.org/0009-0009-2286-3206
Abstract
Remaining useful life (RUL) prediction is critical in
aerospace maintenance for ensuring flight safety, system
availability, and long-term sustainment. Although data
driven and machine-learning (ML) approaches have
enhanced RUL accuracy, they typically provide only point
estimates, either omitting uncertainty quantification (UQ) or
relying on fixed, fleet-wide safety margins. For safety-critical
maintenance decisions, even precise point predictions have
limited value without dependable uncertainty estimates.
To address the need for reliable RUL prediction, this paper
introduces a comprehensive framework that unifies point
estimation and uncertainty quantification while accounting
for aerospace risk preferences. The framework operates by
first using a gradient boosting regressor for its point
predictions. It then uses an asymmetric formulation of
conformalized quantile regression (CQR) to create prediction
intervals that quantify uncertainty. This asymmetric approach
deliberately allocates miscoverage unequally, which
minimizes the risk of overly optimistic predictions and aligns
the model with the operational preference for avoiding late
maintenance intervention.
The framework is evaluated on NASA's Commercial
Modular Aero-Propulsion System Simulation (C-MAPSS)
benchmark dataset. Across all four benchmark subsets, the
framework achieves test RMSE values of 13.26-16.85 with
empirical coverage of 88-92% at 90% nominal coverage.
These results confirm the framework's ability to deliver
accurate point predictions and well-calibrated uncertainty
intervals suitable for safety-critical maintenance planning.
##plugins.themes.bootstrap3.article.details##
Keywords
Remaining Useful Life (RUL) estimation, Prognostics and Health Management (PHM), Uncertainty quantification (UQ), Conformal prediction, Conformalized Quantile Regression (CQR), NASA C-MAPSS dataset, Predictive maintenance, Aircraft engine prognostics
References
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(1), 13466. https://doi.org/10.1038/s41598-023-40315-1
Angelopoulos, A. N., & Bates, S. (2023). Conformal prediction: A gentle introduction. Foundations and Trends in Machine Learning, 16(4), 494–591. https://doi.org/10.1561/2200000101
Barber, R. F., Candès, E. J., Ramdas, A., & Tibshirani, R. J. (2023). Conformal prediction beyond exchangeability. The Annals of Statistics, 51(2), 816–845. https://doi.org/10.1214/23-AOS2276
Chen, J., Jing, H., Chang, Y., & Liu, Q. (2019). Gated recurrent unit based recurrent neural network for remaining useful life prediction of nonlinear deterioration process. Reliability Engineering & System Safety, 185, 372–382. https://doi.org/10.1016/j.ress.2019.01.006
Chen, S., He, J., Wen, P., Zhang, J., Huang, D., & Zhao, S. (2023). Remaining Useful Life Prognostics and Uncertainty Quantification for Aircraft Engines Based on Convolutional Bayesian Long Short-Term Memory Neural Network. 2023 Prognostics and Health Management Conference (PHM), 238–244. https://doi.org/10.1109/PHM58589.2023.00052
Chen, X. (2024). A novel transformer-based DL model enhanced by position-sensitive attention and gated hierarchical LSTM for aero-engine RUL prediction. Scientific Reports, 14(1), 10061. https://doi.org/10.1038/s41598-024-59095-3
Chen, X., Jin, G., Qiu, S., Lu, M., & Yu, D. (2020). Direct Remaining Useful Life Estimation Based on Random Forest Regression. 2020 Global Reliability and Prognostics and Health Management (PHM-Shanghai), 1–7. https://doi.org/10.1109/PHM-Shanghai49105.2020.9281004
Christ, M., Braun, N., Neuffer, J., & Kempa-Liehr, A. W. (2018). Time Series FeatuRe Extraction on basis of Scalable Hypothesis tests (tsfresh – A Python package). Neurocomputing, 307, 72–77. https://doi.org/10.1016/j.neucom.2018.03.067
Cubillo, A., Perinpanayagam, S., & Esperon-Miguez, M. (2016). A review of physics-based models in prognostics: Application to gears and bearings of rotating machinery. Advances in Mechanical Engineering, 8(8), 1–21. https://doi.org/10.1177/1687814016664660
Da Costa, P. R. O., Akçay, A., Zhang, Y., & Kaymak, U. (2023). Attention and long short-term memory network for remaining useful lifetime predictions of turbofan engine degradation. International Journal of Prognostics and Health Management, 10(4). https://doi.org/10.36001/ijphm.2019.v10i4.2623
De Giorgi, M. G., Menga, N., & Ficarella, A. (2023). Exploring Prognostic and Diagnostic Techniques for Jet Engine Health Monitoring: A Review of Degradation Mechanisms and Advanced Prediction Strategies. Energies, 16(6), 2711. https://doi.org/10.3390/en16062711
Deng, S., & Zhou, J. (2024). Prediction of Remaining Useful Life of Aero-engines Based on CNN-LSTM-Attention. International Journal of Computational Intelligence Systems, 17(1), 232. https://doi.org/10.1007/s44196-024-00639-w
Elsherif, S. M., Hafiz, B., Makhlouf, M. A., & Farouk, O. (2025). A deep learning-based prognostic approach for predicting turbofan engine degradation and remaining useful life. Scientific Reports, 15(1), 26251. https://doi.org/10.1038/s41598-025-09155-z
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. https://doi.org/10.3390/app132111893
Faizanbasha, A., & Rizwan, U. (2025). Deep learning-stochastic ensemble for RUL prediction and predictive maintenance with dynamic mission abort policies. Reliability Engineering & System Safety, 259, 110919. https://doi.org/10.1016/j.ress.2025.110919
Frederick, D. K., DeCastro, J. A., & Litt, J. S. (2007). User's guide for the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) (NASA/TM-2007-215026). NASA Glenn Research Center. https://ntrs.nasa.gov/citations/20070034949
Fu, S., & Avdelidis, N. P. (2023). Prognostic and Health Management of Critical Aircraft Systems and Components: An Overview. Sensors, 23(19), 8124. https://doi.org/10.3390/s23198124
Javanmardi, A., & Hüllermeier, E. (2023). Conformal Prediction Intervals for Remaining Useful Lifetime Estimation. International Journal of Prognostics and Health Management, 14(2). https://doi.org/10.36001/ijphm.2023.v14i2.3417
Jiang, L., Zhang, X., Cao, H., & Zhang, Y. (2025). A transformer-based framework with historical data fusion for RUL prediction. Measurement Science and Technology, 36(10), 106103. https://doi.org/10.1088/1361-6501/ae09c2
Li, F., Zhang, L., Chen, B., Gao, D., Cheng, Y., Zhang, X., Yang, Y., Gao, K., Huang, Z., & Peng, J. (2018). A Light Gradient Boosting Machine for Remaining Useful Life Estimation of Aircraft Engines. 2018 21st International Conference on Intelligent Transportation Systems (ITSC), 3562–3567. https://doi.org/10.1109/ITSC.2018.8569801
Li, C. J., & Lee, H. (2005). Gear fatigue crack prognosis using embedded model, gear dynamic model and fracture mechanics. Mechanical Systems and Signal Processing, 19(4), 836–846. https://doi.org/10.1016/j.ymssp.2004.06.007
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. https://doi.org/10.1016/j.ress.2017.11.021
Liao, Y., Zhang, L., & Liu, C. (2018). Uncertainty Prediction of Remaining Useful Life Using Long Short-Term Memory Network Based on Bootstrap Method. 2018 IEEE International Conference on Prognostics and Health Management (ICPHM), 1–8. https://doi.org/10.1109/ICPHM.2018.8448804
Lin, K.-Y., Hong, Y.-H., Li, M.-H., Shi, Y., & Matsuno, K. (2025). Predictive maintenance in industrial systems: an XGBoost-based approach for failure time estimation and resource optimization. Journal of Industrial and Production Engineering. https://doi.org/10.1080/21681015.2025.2519369
Lin, Y.-H., & Li, G.-H. (2022). A Bayesian Deep Learning Framework for RUL Prediction Incorporating Uncertainty Quantification and Calibration. IEEE Transactions on Industrial Informatics, 18(10), 7274–7284. https://doi.org/10.1109/TII.2022.3156965
Liu, L., Wang, L., & Yu, Z. (2021). Remaining Useful Life Estimation of Aircraft Engines Based on Deep Convolution Neural Network and LightGBM Combination Model. International Journal of Computational Intelligence Systems, 14(1), 165. https://doi.org/10.1007/s44196-021-00020-1
Luettig, B., Akhiat, Y., & Daw, Z. (2024). ML meets aerospace: Challenges of certifying airborne AI. Frontiers in Aerospace Engineering, 3, 1475139. https://doi.org/10.3389/fpace.2024.1475139
Marahleh, G., Kheder, A. R. I., & Hamad, H. F. (2006). Creep-life prediction of service-exposed turbine blades. Materials Science, 42(4), 476–481. https://doi.org/10.1007/s11003-006-0103-8
Nguyen, V. D., Kefalas, M., Yang, K., Apostolidis, A., Olhofer, M., Limmer, S., & B¨ack, T. (2019). A Review: Prognostics and Health Management in Automotive and Aerospace. International Journal of Prognostics and Health Management, 10(2). https://doi.org/10.36001/ijphm.2019.v10i2.2730
Ochella, S., Dinmohammadi, F., & Shafiee, M. (2024). Bayesian neural networks for uncertainty quantification in remaining useful life prediction of systems with sensor monitoring. Advances in Mechanical Engineering, 16(7), 16878132241239802. https://doi.org/10.1177/16878132241239802
Peng, C., Chen, Y., Gui, W., Tang, Z., & Li, C. (2022). Remaining useful life prognosis of turbofan engines based on deep feature extraction and fusion. Scientific Reports, 12(1), 6491. https://doi.org/10.1038/s41598-022-10191-2
Ramasso, E., & Saxena, A. (2014). Review and Analysis of Algorithmic Approaches Developed for Prognostics on CMAPSS Dataset. Annual Conference of the PHM Society, 6(1). https://doi.org/10.36001/phmconf.2014.v6i1.2512
Rengasamy, D., Rothwell, B., & Figueredo, G. P. (2020). Asymmetric Loss Functions for Deep Learning Early Predictions of Remaining Useful Life in Aerospace Gas Turbine Engines. 2020 International Joint Conference on Neural Networks (IJCNN), 1–7. https://doi.org/10.1109/IJCNN48605.2020.9207051
Romano, Y., Patterson, E., & Candès, E. J. (2019). Conformalized quantile regression. In Advances in Neural Information Processing Systems (Vol. 32, pp. 3543–3553). https://proceedings.neurips.cc/paper/2019/hash/5103c3584b063c431bd1268e9b5e76fb-Abstract.html
Sankararaman, S. (2015). Significance, interpretation, and quantification of uncertainty in prognostics and remaining useful life prediction. Mechanical Systems and Signal Processing, 52–53, 228–247. https://doi.org/10.1016/j.ymssp.2014.05.029
Sankararaman, S., Daigle, M. J., & Goebel, K. (2014). Uncertainty Quantification in Remaining Useful Life Prediction Using First-Order Reliability Methods. IEEE Transactions on Reliability, 63(2), 603–619. https://doi.org/10.1109/TR.2014.2313801
Sankararaman, S., & Goebel, K. (2013). Why is the Remaining Useful Life Prediction Uncertain? Annual Conference of the PHM Society, 5(1). https://doi.org/10.36001/phmconf.2013.v5i1.2263
Sankararaman, S., & Goebel, K. (2020). Uncertainty in Prognostics and Systems Health Management. International Journal of Prognostics and Health Management, 6(4). https://doi.org/10.36001/ijphm.2015.v6i4.2319
Saxena, A., Celaya, J., Balaban, E., Goebel, K., Saha, B., Saha, S., & Schwabacher, M. (2008). Metrics for evaluating performance of prognostic techniques. 2008 International Conference on Prognostics and Health Management, 1–17. https://doi.org/10.1109/PHM.2008.4711436
Saxena, A., Celaya, J., Saha, B., Saha, S., & Goebel, K. (2010). Evaluating prognostics performance for algorithms incorporating uncertainty estimates. 2010 IEEE Aerospace Conference, 1–11. https://doi.org/10.1109/AERO.2010.5446828
Shafer, G., & Vovk, V. (2008). A tutorial on conformal prediction. Journal of Machine Learning Research, 9, 371–421. https://doi.org/10.5555/1390681.1390693
Song, Y., Gao, S., Li, Y., Jia, L., Li, Q., & Pang, F. (2021). Distributed Attention-Based Temporal Convolutional Network for Remaining Useful Life Prediction. IEEE Internet of Things Journal, 8(12), 9594–9602. https://doi.org/10.1109/JIOT.2020.3004452
Sun, J., Zheng, L., Huang, Y., & Ge, Y. (2022). Remaining Useful Life Prediction Based on CNN-BGRU-SA. Journal of Physics: Conference Series, 2405(1), 012007. https://doi.org/10.1088/1742-6596/2405/1/012007
Wang, J., Wen, G., Yang, S., & Liu, Y. (2018). Remaining Useful Life Estimation in Prognostics Using Deep Bidirectional LSTM Neural Network. 2018 Prognostics and System Health Management Conference (PHM-Chongqing), 1037–1042. https://doi.org/10.1109/PHM-Chongqing.2018.00184
Wu, J., Hu, K., Cheng, Y., Zhu, H., Shao, X., & Wang, Y. (2020). Data-driven remaining useful life prediction via multiple sensor signals and deep long short-term memory neural network. ISA Transactions, 97, 241–250. https://doi.org/10.1016/j.isatra.2019.07.004
Xia, T., Song, Y., Zheng, Y., Pan, E., & Xi, L. (2020). An ensemble framework based on convolutional bi-directional LSTM with multiple time windows for remaining useful life estimation. Computers in Industry, 115, 103182. https://doi.org/10.1016/j.compind.2019.103182
Zhao, Z., Wu, J., Wong, D., Sun, C., & Yan, R. (2020). Probabilistic Remaining Useful Life Prediction Based on Deep Convolutional Neural Network. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3717738
Zheng, S., Ristovski, K., Farahat, A., & Gupta, C. (2017). Long Short-Term Memory Network for Remaining Useful Life estimation. 2017 IEEE International Conference on Prognostics and Health Management (ICPHM), 88–95. https://doi.org/10.1109/ICPHM.2017.7998311
Zio, E. (2022). Prognostics and Health Management (PHM): Where are we and where do we (need to) go in theory and practice. Reliability Engineering & System Safety, 218, 108119. https://doi.org/10.1016/j.ress.2021.108119
Zio, E., & Peloni, G. (2011). Particle filtering prognostic estimation of the remaining useful life of nonlinear components. Reliability Engineering & System Safety, 96(3), 403–409. https://doi.org/10.1016/j.ress.2010.08.009
Angelopoulos, A. N., & Bates, S. (2023). Conformal prediction: A gentle introduction. Foundations and Trends in Machine Learning, 16(4), 494–591. https://doi.org/10.1561/2200000101
Barber, R. F., Candès, E. J., Ramdas, A., & Tibshirani, R. J. (2023). Conformal prediction beyond exchangeability. The Annals of Statistics, 51(2), 816–845. https://doi.org/10.1214/23-AOS2276
Chen, J., Jing, H., Chang, Y., & Liu, Q. (2019). Gated recurrent unit based recurrent neural network for remaining useful life prediction of nonlinear deterioration process. Reliability Engineering & System Safety, 185, 372–382. https://doi.org/10.1016/j.ress.2019.01.006
Chen, S., He, J., Wen, P., Zhang, J., Huang, D., & Zhao, S. (2023). Remaining Useful Life Prognostics and Uncertainty Quantification for Aircraft Engines Based on Convolutional Bayesian Long Short-Term Memory Neural Network. 2023 Prognostics and Health Management Conference (PHM), 238–244. https://doi.org/10.1109/PHM58589.2023.00052
Chen, X. (2024). A novel transformer-based DL model enhanced by position-sensitive attention and gated hierarchical LSTM for aero-engine RUL prediction. Scientific Reports, 14(1), 10061. https://doi.org/10.1038/s41598-024-59095-3
Chen, X., Jin, G., Qiu, S., Lu, M., & Yu, D. (2020). Direct Remaining Useful Life Estimation Based on Random Forest Regression. 2020 Global Reliability and Prognostics and Health Management (PHM-Shanghai), 1–7. https://doi.org/10.1109/PHM-Shanghai49105.2020.9281004
Christ, M., Braun, N., Neuffer, J., & Kempa-Liehr, A. W. (2018). Time Series FeatuRe Extraction on basis of Scalable Hypothesis tests (tsfresh – A Python package). Neurocomputing, 307, 72–77. https://doi.org/10.1016/j.neucom.2018.03.067
Cubillo, A., Perinpanayagam, S., & Esperon-Miguez, M. (2016). A review of physics-based models in prognostics: Application to gears and bearings of rotating machinery. Advances in Mechanical Engineering, 8(8), 1–21. https://doi.org/10.1177/1687814016664660
Da Costa, P. R. O., Akçay, A., Zhang, Y., & Kaymak, U. (2023). Attention and long short-term memory network for remaining useful lifetime predictions of turbofan engine degradation. International Journal of Prognostics and Health Management, 10(4). https://doi.org/10.36001/ijphm.2019.v10i4.2623
De Giorgi, M. G., Menga, N., & Ficarella, A. (2023). Exploring Prognostic and Diagnostic Techniques for Jet Engine Health Monitoring: A Review of Degradation Mechanisms and Advanced Prediction Strategies. Energies, 16(6), 2711. https://doi.org/10.3390/en16062711
Deng, S., & Zhou, J. (2024). Prediction of Remaining Useful Life of Aero-engines Based on CNN-LSTM-Attention. International Journal of Computational Intelligence Systems, 17(1), 232. https://doi.org/10.1007/s44196-024-00639-w
Elsherif, S. M., Hafiz, B., Makhlouf, M. A., & Farouk, O. (2025). A deep learning-based prognostic approach for predicting turbofan engine degradation and remaining useful life. Scientific Reports, 15(1), 26251. https://doi.org/10.1038/s41598-025-09155-z
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. https://doi.org/10.3390/app132111893
Faizanbasha, A., & Rizwan, U. (2025). Deep learning-stochastic ensemble for RUL prediction and predictive maintenance with dynamic mission abort policies. Reliability Engineering & System Safety, 259, 110919. https://doi.org/10.1016/j.ress.2025.110919
Frederick, D. K., DeCastro, J. A., & Litt, J. S. (2007). User's guide for the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) (NASA/TM-2007-215026). NASA Glenn Research Center. https://ntrs.nasa.gov/citations/20070034949
Fu, S., & Avdelidis, N. P. (2023). Prognostic and Health Management of Critical Aircraft Systems and Components: An Overview. Sensors, 23(19), 8124. https://doi.org/10.3390/s23198124
Javanmardi, A., & Hüllermeier, E. (2023). Conformal Prediction Intervals for Remaining Useful Lifetime Estimation. International Journal of Prognostics and Health Management, 14(2). https://doi.org/10.36001/ijphm.2023.v14i2.3417
Jiang, L., Zhang, X., Cao, H., & Zhang, Y. (2025). A transformer-based framework with historical data fusion for RUL prediction. Measurement Science and Technology, 36(10), 106103. https://doi.org/10.1088/1361-6501/ae09c2
Li, F., Zhang, L., Chen, B., Gao, D., Cheng, Y., Zhang, X., Yang, Y., Gao, K., Huang, Z., & Peng, J. (2018). A Light Gradient Boosting Machine for Remaining Useful Life Estimation of Aircraft Engines. 2018 21st International Conference on Intelligent Transportation Systems (ITSC), 3562–3567. https://doi.org/10.1109/ITSC.2018.8569801
Li, C. J., & Lee, H. (2005). Gear fatigue crack prognosis using embedded model, gear dynamic model and fracture mechanics. Mechanical Systems and Signal Processing, 19(4), 836–846. https://doi.org/10.1016/j.ymssp.2004.06.007
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. https://doi.org/10.1016/j.ress.2017.11.021
Liao, Y., Zhang, L., & Liu, C. (2018). Uncertainty Prediction of Remaining Useful Life Using Long Short-Term Memory Network Based on Bootstrap Method. 2018 IEEE International Conference on Prognostics and Health Management (ICPHM), 1–8. https://doi.org/10.1109/ICPHM.2018.8448804
Lin, K.-Y., Hong, Y.-H., Li, M.-H., Shi, Y., & Matsuno, K. (2025). Predictive maintenance in industrial systems: an XGBoost-based approach for failure time estimation and resource optimization. Journal of Industrial and Production Engineering. https://doi.org/10.1080/21681015.2025.2519369
Lin, Y.-H., & Li, G.-H. (2022). A Bayesian Deep Learning Framework for RUL Prediction Incorporating Uncertainty Quantification and Calibration. IEEE Transactions on Industrial Informatics, 18(10), 7274–7284. https://doi.org/10.1109/TII.2022.3156965
Liu, L., Wang, L., & Yu, Z. (2021). Remaining Useful Life Estimation of Aircraft Engines Based on Deep Convolution Neural Network and LightGBM Combination Model. International Journal of Computational Intelligence Systems, 14(1), 165. https://doi.org/10.1007/s44196-021-00020-1
Luettig, B., Akhiat, Y., & Daw, Z. (2024). ML meets aerospace: Challenges of certifying airborne AI. Frontiers in Aerospace Engineering, 3, 1475139. https://doi.org/10.3389/fpace.2024.1475139
Marahleh, G., Kheder, A. R. I., & Hamad, H. F. (2006). Creep-life prediction of service-exposed turbine blades. Materials Science, 42(4), 476–481. https://doi.org/10.1007/s11003-006-0103-8
Nguyen, V. D., Kefalas, M., Yang, K., Apostolidis, A., Olhofer, M., Limmer, S., & B¨ack, T. (2019). A Review: Prognostics and Health Management in Automotive and Aerospace. International Journal of Prognostics and Health Management, 10(2). https://doi.org/10.36001/ijphm.2019.v10i2.2730
Ochella, S., Dinmohammadi, F., & Shafiee, M. (2024). Bayesian neural networks for uncertainty quantification in remaining useful life prediction of systems with sensor monitoring. Advances in Mechanical Engineering, 16(7), 16878132241239802. https://doi.org/10.1177/16878132241239802
Peng, C., Chen, Y., Gui, W., Tang, Z., & Li, C. (2022). Remaining useful life prognosis of turbofan engines based on deep feature extraction and fusion. Scientific Reports, 12(1), 6491. https://doi.org/10.1038/s41598-022-10191-2
Ramasso, E., & Saxena, A. (2014). Review and Analysis of Algorithmic Approaches Developed for Prognostics on CMAPSS Dataset. Annual Conference of the PHM Society, 6(1). https://doi.org/10.36001/phmconf.2014.v6i1.2512
Rengasamy, D., Rothwell, B., & Figueredo, G. P. (2020). Asymmetric Loss Functions for Deep Learning Early Predictions of Remaining Useful Life in Aerospace Gas Turbine Engines. 2020 International Joint Conference on Neural Networks (IJCNN), 1–7. https://doi.org/10.1109/IJCNN48605.2020.9207051
Romano, Y., Patterson, E., & Candès, E. J. (2019). Conformalized quantile regression. In Advances in Neural Information Processing Systems (Vol. 32, pp. 3543–3553). https://proceedings.neurips.cc/paper/2019/hash/5103c3584b063c431bd1268e9b5e76fb-Abstract.html
Sankararaman, S. (2015). Significance, interpretation, and quantification of uncertainty in prognostics and remaining useful life prediction. Mechanical Systems and Signal Processing, 52–53, 228–247. https://doi.org/10.1016/j.ymssp.2014.05.029
Sankararaman, S., Daigle, M. J., & Goebel, K. (2014). Uncertainty Quantification in Remaining Useful Life Prediction Using First-Order Reliability Methods. IEEE Transactions on Reliability, 63(2), 603–619. https://doi.org/10.1109/TR.2014.2313801
Sankararaman, S., & Goebel, K. (2013). Why is the Remaining Useful Life Prediction Uncertain? Annual Conference of the PHM Society, 5(1). https://doi.org/10.36001/phmconf.2013.v5i1.2263
Sankararaman, S., & Goebel, K. (2020). Uncertainty in Prognostics and Systems Health Management. International Journal of Prognostics and Health Management, 6(4). https://doi.org/10.36001/ijphm.2015.v6i4.2319
Saxena, A., Celaya, J., Balaban, E., Goebel, K., Saha, B., Saha, S., & Schwabacher, M. (2008). Metrics for evaluating performance of prognostic techniques. 2008 International Conference on Prognostics and Health Management, 1–17. https://doi.org/10.1109/PHM.2008.4711436
Saxena, A., Celaya, J., Saha, B., Saha, S., & Goebel, K. (2010). Evaluating prognostics performance for algorithms incorporating uncertainty estimates. 2010 IEEE Aerospace Conference, 1–11. https://doi.org/10.1109/AERO.2010.5446828
Shafer, G., & Vovk, V. (2008). A tutorial on conformal prediction. Journal of Machine Learning Research, 9, 371–421. https://doi.org/10.5555/1390681.1390693
Song, Y., Gao, S., Li, Y., Jia, L., Li, Q., & Pang, F. (2021). Distributed Attention-Based Temporal Convolutional Network for Remaining Useful Life Prediction. IEEE Internet of Things Journal, 8(12), 9594–9602. https://doi.org/10.1109/JIOT.2020.3004452
Sun, J., Zheng, L., Huang, Y., & Ge, Y. (2022). Remaining Useful Life Prediction Based on CNN-BGRU-SA. Journal of Physics: Conference Series, 2405(1), 012007. https://doi.org/10.1088/1742-6596/2405/1/012007
Wang, J., Wen, G., Yang, S., & Liu, Y. (2018). Remaining Useful Life Estimation in Prognostics Using Deep Bidirectional LSTM Neural Network. 2018 Prognostics and System Health Management Conference (PHM-Chongqing), 1037–1042. https://doi.org/10.1109/PHM-Chongqing.2018.00184
Wu, J., Hu, K., Cheng, Y., Zhu, H., Shao, X., & Wang, Y. (2020). Data-driven remaining useful life prediction via multiple sensor signals and deep long short-term memory neural network. ISA Transactions, 97, 241–250. https://doi.org/10.1016/j.isatra.2019.07.004
Xia, T., Song, Y., Zheng, Y., Pan, E., & Xi, L. (2020). An ensemble framework based on convolutional bi-directional LSTM with multiple time windows for remaining useful life estimation. Computers in Industry, 115, 103182. https://doi.org/10.1016/j.compind.2019.103182
Zhao, Z., Wu, J., Wong, D., Sun, C., & Yan, R. (2020). Probabilistic Remaining Useful Life Prediction Based on Deep Convolutional Neural Network. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3717738
Zheng, S., Ristovski, K., Farahat, A., & Gupta, C. (2017). Long Short-Term Memory Network for Remaining Useful Life estimation. 2017 IEEE International Conference on Prognostics and Health Management (ICPHM), 88–95. https://doi.org/10.1109/ICPHM.2017.7998311
Zio, E. (2022). Prognostics and Health Management (PHM): Where are we and where do we (need to) go in theory and practice. Reliability Engineering & System Safety, 218, 108119. https://doi.org/10.1016/j.ress.2021.108119
Zio, E., & Peloni, G. (2011). Particle filtering prognostic estimation of the remaining useful life of nonlinear components. Reliability Engineering & System Safety, 96(3), 403–409. https://doi.org/10.1016/j.ress.2010.08.009
Section
Technical Papers