PHM for Spacecraft Propulsion Systems: Developing Resilient Models for Real-World Challenges

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Published Jun 27, 2024
Takanobu Minami Dai-Yan Ji Jay Lee

Abstract

This paper extends the research presented at the Prognostics and Health Management (PHM) Asia-Pacific 2023 Conference Data Challenge, focusing on a more pragmatic approach to spacecraft propulsion system health assessment. While the previous competition saw a variety of solutions, they predominantly relied on the assumption of highly stable initial hydraulic conditions – an idealization seldom met in real-world scenarios. In practical settings, factors such as operational noise, recent operational states, and ambient environmental conditions significantly disrupt this stability, rendering such solutions less feasible. Addressing this gap, our current study introduces a novel diagnostic model capable of valve faults without depending on the initial stable state of hydraulics. This approach marks a significant shift from our previous methodology, which primarily utilized similarity measures and physics-inspired features to classify health states and identify solenoid valve faults in spacecraft propulsion systems. The proposed model in this paper is validated against a diverse set of conditions, emphasizing its robustness and applicability in fluctuating real-world scenarios. Our findings demonstrate that the new model not only effectively diagnoses system health under varied and less controlled conditions but also enhances the practicality of spacecraft health management, offering a more adaptable solution in the face of operational uncertainties.

How to Cite

Minami, T. ., Ji, D.-Y., & Lee, J. (2024). PHM for Spacecraft Propulsion Systems: Developing Resilient Models for Real-World Challenges. PHM Society European Conference, 8(1), 7. https://doi.org/10.36001/phme.2024.v8i1.4110
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Keywords

PHM, Spacecraft, Fault diagnosis, Similarity-based

References
Baptista, M. L., & Henriques, E. M. (2022). 1D-DGANPHM: A 1-D denoising GAN for Prognostics and Health Management with an application to turbofan. Applied Soft Computing, 131, 109785.

Boškoski, P., & Urevc, A. (2011). Bearing fault detection with application to PHM Data Challenge. International Journal of Prognostics and Health Management Volume 2 (color), 32.

Kato, Y., Kato, T., & Tanaka, T. (2023, September).

Anomaly detection in spacecraft propulsion system using time series classification based on k-nn. In PHM Society Asia-Pacific Conference (Vol. 4, No. 1).

Lee, S. K., Lee, J., Lee, S., Kim, B., Kim, Y. C., Lee, J., & Youn, B. D. (2023, September). Hybrid approach of xgboost and rule-based model for fault detection and severity estimation in spacecraft propulsion system. In PHM Society Asia-Pacific Conference (Vol. 4, No. 1). Liu, H., Zhou, J., Zheng, Y., Jiang, W., & Zhang, Y. (2018).

Fault diagnosis of rolling bearings with recurrent neural network-based autoencoders. ISA transactions, 77, 167178.

Minami, T., & Lee, J. (2023, September). Phm for spacecraft propulsion systems: Similarity-based model and physics-inspired features. In PHM Society Asia-Pacific Conference (Vol. 4, No. 1).

Mubarak, A., Asmelash, M., Azhari, A., Haggos, F. Y., & Mulubrhan, F. (2023). Machine health management system using moving average feature with bidirectional long-short term memory. Journal of Computing and Information Science in Engineering, 23(3), 031002. Najafabadi, M. M., Villanustre, F., Khoshgoftaar, T. M., Seliya, N., Wald, R., & Muharemagic, E. (2015). Deep learning applications and challenges in big data analytics. Journal of big data, 2, 1-21.

PHMAP 2023 Secretariat. PHM Asia Pacific 2023 Conference Data Challenge. (2023, Aug 4). https://phmap.jp/program-data/ Tominaga, K., Daimon, Y., Toyama, M., Adachi, K., Tsutsumi, S., Omata, N., & Nagata, T. (2023, September). Dataset generation based on 1D-CAE modeling for fault diagnostics in a spacecraft propulsion system. In PHM Society Asia-Pacific Conference (Vol. 4, No. 1).
Section
Technical Papers