PHM for Spacecraft Propulsion Systems: Similarity-Based Model and Physics-Inspired Features



Published Sep 4, 2023
Takanobu Minami Jay Lee


This paper presents a methodology designed for the Prognostics and Health Management (PHM) Asia-Pacific 2023 Conference Data Challenge. In particular, this study targets the health assessment of spacecraft propulsion systems. The challenge involved analyzing and categorizing a simulation-generated dataset that included four unique spacecraft and multiple health conditions, such as normal operation, bubble anomalies, and solenoid valve faults in various system locations. The proposed approach uses a two- step process. First, a model based on similarity measures is employed to classify the data into one of four health states. Then, a model incorporating physics-inspired features is utilized in solenoid valve faults to identify the fault location and estimate the valve opening ratio. The validity of the model is confirmed through cross-validation with the training dataset, which achieved a flawless total score across all permutations. Our method effectively categorizes the test data, including cases from a spacecraft not covered in the training, thereby securing a top position in the competition. The findings highlight the strength of our proposed model, which uses physics-inspired features to predict valve opening ratios, proving useful in managing and interpreting complex, unfamiliar spacecraft health data.

Abstract 225 | PDF Downloads 190



Data challenge, PHM, Similarity-Based models, Physics-Inspired Features

Berndt, D. J., & Clifford, J. (1994, July). Using dynamic time warping to find patterns in time series. In KDD workshop (Vol. 10, No. 16, pp. 359-370).

Duan, Y., Li, H., Zhang, N., & Bai, Y. (2021, October). An improved similarity matching bidirectional gated recurrent unit autoencoder scheme for remaining useful life prediction. In 2021 Global Reliability and Prognostics and Health Management (PHM-Nanjing) (pp. 1-8). IEEE.

Hendrickx, K., Meert, W., Cornelis, B., Gryllias, K., & Davis, J. (2020). Similarity-based anomaly score for fleet-based condition monitoring. In Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM (Vol. 12, No. 1). PHM Society.

Lee, J., Wu, F., Zhao, W., Ghaffari, M., Liao, L., & Siegel, D. (2014). Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications. Mechanical systems and signal processing, 42(1-2), 314-334.

Ompusunggu, A. P., & Hostens, E. (2021, February). Physics-Inspired Feature Engineering for Condition Monitoring of Alternating Current-Powered Solenoid-Operated Valves. In International Conference on Maintenance, Condition Monitoring and Diagnostics (pp. 139-151). Singapore: Springer Nature Singapore.

Pukelsheim, F. (1994). The three sigma rule. The American Statistician, 48(2), 88-91.

Senanayaka, A., Al Mamun, A., Bond, G., Tian, W., Wang, H., Fuller, S., ... & Bian, L. (2022). Similarity-based Multi-source Transfer Learning Approach for Time Series Classification. International Journal of Prognostics and Health Management, 13(2).

Tsui, K. L., Chen, N., Zhou, Q., Hai, Y., & Wang, W. (2015). Prognostics and health management: A review on data driven approaches. Mathematical Problems in Engineering, 2015.

PHMAP 2023 Secretariat. PHM Asia Pacific 2023 Conference Data Challenge. (2022, Aug 4).

Wang, Tianyi, et al. "A similarity-based prognostics approach for remaining useful life estimation of engineered systems." 2008 international conference on prognostics and health management. IEEE, 2008.
Data Challenge Papers