PhD Symposium - Interpretable and Uncertainty-Aware Hybrid Prognostics Using Multimodal Knowledge for RUL Prediction
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https://orcid.org/0009-0008-8229-2145
Dimitrios Zarouchas Manuel Arias Chao
Abstract
Unforeseen technical failures contribute significantly to airline delays, highlighting the need for predictive maintenance. However, developing reliable prognostic models in aviation is challenging due to strict safety requirements, limited labeled data, and the need for interpretable and trustworthy predictions. This research proposes a hybrid framework for remaining useful life (RUL) prediction that integrates multimodal domain knowledge available to airlines, such as sensor data, contextual information and reliability insights, into interpretable and uncertainty-aware algorithms. To this end, the proposed framework resorts to unsupervised degradation extraction with knowledge-informed autoencoders and supports extensions for failure mode segmentation. Initial experiments on a benchmark dataset show promising results, and application to real-world commercial aircraft data is planned to further validate the approach.
How to Cite
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Prognostics, Aviation, RUL Estimation, Reliability Informed Deep Learning
Arias Chao, M., Kulkarni, C., Goebel, K., & Fink, O. (2022, January). Fusing physics-based and deep learning models for prognostics. Reliability Engineering & System Safety, 217, 107961.
Bajarunas, K., Baptista, M., Goebel, K., & Chao, M. A. (2023, October). Generic Hybrid Models for Prognostics of Complex Systems. Annual Conference of the PHM Society, 15(1).
Bermejo-Barbanoj, C., Moya, B., Bad´ıas, A., Chinesta, F., & Cueto, E. (2024, February). Thermodynamics informed super-resolution of scarce temporal dynamics data.
Biggio, L., Bendinelli, T., Kulkarni, C., & Fink, O. (2023, September). Ageing-aware battery discharge prediction with deep learning. Applied Energy, 346, 121229.
Dersin, P., Bajarunas, K., & Chao, M. A. (2024). Analytical Modeling of Health Indices for Prognostics and Health management.
Eurocontrol. (2024, March). Annual Network Operations Report 2023 | EUROCONTROL (Tech. Rep.).
Fink, O., Wang, Q., Svens´en, M., Dersin, P., Lee, W.-J., & Ducoffe, M. (2020, June). Potential, challenges and future directions for deep learning in prognostics and health management applications. Engineering Applications of Artificial Intelligence, 92, 103678.
Guo, L., Li, N., Jia, F., Lei, Y., & Lin, J. (2017, May). A recurrent neural network based health indicator for remaining useful life prediction of bearings. Neurocomputing, 240, 98–109.
Karniadakis, G. E., Kevrekidis, I. G., Lu, L., Perdikaris, P., Wang, S., & Yang, L. (2021, June). Physics-informed machine learning. Nature Reviews Physics, 3(6), 422– 440.
Kumar, A., Parkash, C., Vashishtha, G., Tang, H., Kundu, P., & Xiang, J. (2022, May). State-space modeling and novel entropy-based health indicator for dynamic degradation monitoring of rolling element bearing. Reliability Engineering & System Safety, 221, 108356.
Li, X., Teng, W., Peng, D., Ma, T., Wu, X., & Liu, Y. (2023, May). Feature fusion model based health indicator construction and self-constraint state-space estimator for remaining useful life prediction of bearings in wind turbines. Reliability Engineering & System Safety, 233, 109124.
Nemani, V., Biggio, L., Huan, X., Hu, Z., Fink, O., Tran, A., . . . Hu, C. (2023, September). Uncertainty Quantification in Machine Learning for Engineering Design and Health Prognostics: A Tutorial. arXiv.
Qin, Y., Yang, J., Zhou, J., Pu, H., & Mao, Y. (2023, April). A new supervised multi-head self-attention autoencoder for health indicator construction and similarity-based machinery RUL prediction. Advanced Engineering Informatics, 56, 101973.
Song, C., & Liu, K. (2018, October). Statistical degradation modeling and prognostics of multiple sensor signals via data fusion: A composite health index approach. IISE Transactions, 50(10), 853–867.
Soudain, G. (2024, March). EASA Concept Paper: Guidance for Level 1 & 2 machine learning applications.
Verhagen, W. J. C., Santos, B. F., Freeman, F., van Kessel, P., Zarouchas, D., Loutas, T., . . . Heiets, I. (2023, September). Condition-Based Maintenance in Aviation: Challenges and Opportunities. Aerospace, 10(9), 762.
Walthall, R., & Rajamani, R. (2018, September). The Role of PHM at Commercial Airlines. In M. G. Pecht & M. Kang (Eds.), Prognostics and Health Management of Electronics (1st ed., pp. 503–534). Wiley.
Wang, Q., Qin, K., Lu, B., Sun, H., & Shu, P. (2023, August). Time-feature attention-based convolutional autoencoder for flight feature extraction. Scientific Reports, 13(1), 14175.
Zhang, Y., Zhang, C., Wang, S., Dui, H., & Chen, R. (2024, January). Health indicators for remaining useful life prediction of complex systems based on long shortterm memory network and improved particle filter. Reliability Engineering & System Safety, 241, 109666.
Zytek, A., Pido, S., & Veeramachaneni, K. (2024, May). LLMs for XAI: Future Directions for Explaining Explanations.
Zytek, A., Wang, W.-E., Liu, D., Berti-Equille, L., & Veeramachaneni, K. (2023, December). Pyreal: A Framework for Interpretable ML Explanations. arXiv.
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