References
- Hong-feng, W. (2012). Prognostics and Health Management for Complex system Based on Fusion of Model-based approach and Data-driven approach. Physics Procedia, 24, 828-831.
- Aydemir, G., & Acar, B. (2020). Anomaly monitoring improves remaining useful life estimation of industrial machinery. Journal of Manufacturing Systems, 56, 463-469.
- Sankararaman, S. (2015). Significance, interpretation, and quantification of uncertainty in prognostics and remaining useful life prediction. Mechanical Systems and Signal Processing, 52, 228-247.
- Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338-353.
- Dempster, A. P. (1967). Upper and lower probabilities induced by a multivalued mapping. Ann. Math. Stat. 325–339.
- Shafer, G. (1976). A mathematical theory of evidence (Vol. 42). Princeton university press.
- Amini, A., Schwarting, W., Soleimany, A., & Rus, D. (2020). Deep evidential regression. Advances in Neural Information Processing Systems, 33, 14927-14937.
- Frederick, D. K., DeCastro, J. A., & Litt, J. S. (2007). User’s guide for the commercial modular aero-propulsion system simulation (C-MAPSS) (No. E-16205).
- Saxena, A., Goebel, K., Simon, D., & Eklund, N. (2008). Damage Propagation Modeling for Aircraft Engine Run-to-Failure Simulation. In Proceedings of the International Conference on Prognostics and Health Management, 1–9.
- LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
- Wang, Z., Liu, N., & Guo, Y. (2021). Adaptive sliding window LSTM NN based RUL prediction for lithium-ion batteries integrating LTSA feature reconstruction. Neurocomputing, 466, 178-189.
- Zhao, B., & Yuan, Q. (2021). A novel deep learning scheme for multi-condition remaining useful life prediction of rolling element bearings. Journal of Manufacturing Systems, 61, 450-460.
- Liu, K., Shang, Y., Ouyang, Q., & Widanage, W. D. (2020). A data-driven approach with uncertainty quantification for predicting future capacities and remaining useful life of lithium-ion battery. IEEE Transactions on Industrial Electronics, 68(4), 3170-3180.
- Gou, B., Xu, Y., & Feng, X. (2020). State-of-health estimation and remaining-useful-life prediction for lithium-ion battery using a hybrid data-driven method. IEEE Transactions on Vehicular Technology, 69(10), 10854-10867.
- Zhang, Y., Xiong, R., He, H., & Pecht, M. G. (2018). Lithium-ion battery remaining useful life prediction with Box–Cox transformation and Monte Carlo simulation. IEEE Transactions on Industrial Electronics, 66(2), 1585-1597.
- 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). Springer International Publishing.
- Wen, L., Dong, Y., & Gao, L. (2019). A new ensemble residual convolutional neural network for remaining useful life estimation. Math. Biosci. Eng, 16(2), 862-880.
- Khan, A., Sohail, A., Zahoora, U., & Qureshi, A. S. (2020). A survey of the recent architectures of deep convolutional neural networks. Artificial intelligence review, 53, 5455-5516.
- Li, J., Li, X., & He, D. (2019). A directed acyclic graph network combined with CNN and LSTM for remaining useful life prediction. IEEE Access, 7, 75464-75475.
- Muneer, A., Taib, S. M., Fati, S. M., & Alhus-sian, H. (2021). Deep-learning based prognosis approach for remaining useful life prediction of turbofan engine. Symmetry, 13(10), 1861.
- 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.
- Gugulothu, N., Tv, V., Malhotra, P., Vig, L., Agarwal, P., & Shroff, G. (2017). Predicting remaining useful life using time series embeddings based on recurrent neural networks. arXiv preprint arXiv:1709.01073.
- Soualhi, M., Nguyen, K. T., Medjaher, K., Nejjari, F., Puig, V., Blesa, J., & Marlasca, F. (2023). Dealing with prognostics uncertainties: Combination of direct and recursive remaining useful life estimations. Computers in Industry, 144, 103766.
- Chang, Y., Zou, J., Fan, S., Peng, C., & Fang, H. (2022). Remaining useful life prediction of degraded system with the capability of uncertainty management. Mechanical Systems and Signal Processing, 177, 109166.
- Prabhakar, S., & Cheng, C. K. (2009). Data uncertainty management in sensor networks. Encyclopedia of Database Systems.
- Gal, Y., & Ghahramani, Z. (2016, June). Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In international conference on machine learning (pp. 1050-1059). PMLR.
- Postels, J., Ferroni, F., Coskun, H., Navab, N., & Tombari, F. (2019). Sampling-free epistemic uncertainty estimation using approximated variance propagation. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 2931-2940).
- Geifman, Y., Uziel, G., & El-Yaniv, R. (2018). Bias-reduced uncertainty estimation for deep neural classifiers. arXiv preprint arXiv:1805.08206.
- Denœux, T. (2019). Logistic regression, neural networks and Dempster–Shafer theory: A new perspective. Knowledge-Based Systems, 176, 54-67.
- Sensoy, M., Kaplan, L., & Kandemir, M. (2018). Evidential deep learning to quantify classification uncertainty. Advances in neural information processing systems, 31.
- Lorieul, T. (2020). Uncertainty in predictions of deep learning models for fine-grained classification (Doctoral dissertation, Université Montpellier).
- MacKay, D. J. (1995). Bayesian neural networks and density networks. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 354(1), 73-80.
- Mendes-Moreira, J., Soares, C., Jorge, A. M., & Sousa, J. F. D. (2012). Ensemble approaches for regression: A survey. Acm computing surveys (csur), 45(1), 1-40.
- Parisi, G., & Shankar, R. (1988). Statistical field theory.
- Jiang, Ke, Brian Kulis, and Michael Jordan. “Small-variance asymptotics for exponential family Dirichlet process mixture models.” Advances in Neural Information Processing Systems 25 (2012).