A Health Index for Satellite System Based on Characteristics of Telemetry Data



Published Sep 4, 2023
Shun Katsube Hironori Sahara


Since satellites are non-repairable systems, it is important to detect anomalies early to prevent failures. Data-driven approaches to anomaly detection, which have been actively
studied in recent years, have problems such as low explainability and insufficient training data in initial operation. Thus, we propose a health index that is commonly available for all satellites. It is possible to share training data and examples of anomalies by monitoring
health status with the same index across different satellites. In this study, we extended our health index defined for the satellite power system to the entire satellite system. Then,
we applied the health index to the operational data of the Suzaku satellite and confirmed that the index is useful for anomaly detection.

Abstract 14 | PDF Downloads 32



Anomaly detection, Feature extraction, Prognostics and health management, Satellite, Telemetry data

Chen, J., Pi, D., Wu, Z., Zhao, X., Pan, Y., & Zhang, Q. (2021). Imbalanced Satellite Telemetry Data Anomaly Detection Model Based on Bayesian LSTM. Acta Astronautica. vol. 180, pp. 232-242.

ISAS/JAXA (2015). Log of important operation events and happenings. http://www.astro.isas.jaxa.jp/suzaku/log/operation/.

ISAS/JAXA (2016). Data archive and transfer system (DARTS). https://darts.isas.jaxa.jp/astro/suzaku/.

Katsube, S., & Sahara, H. (2022). Proposal of an Index of Satellite Health for Anomaly Detection. 73rd International Astronautical Congress. September 18-22, Paris, France.

Maeda, Y. (2016). A Story of the Operation of the Suzaku Battery and the Solar Array Paddle, The Astronomical Herald, vol. 109, pp. 14-20.

Ruff, L., Kauffmann, J. R., Vandermeulen, R. A., Montavon, G., Samek, W., Kloft, M., Dietterich, T. G., & Muller, K.-R. (2021). A Unifying Review of Deep and Shallow Anomaly Detection, Proceedings of the IEEE, vol. 109, pp. 756-795.

Takaki, R., Hashimoto, M., Honda, H., & Choki, A. (2006). ISACS-DOC: Automatic Monitoring and Diagnostic System for Scientific Satellite, Japanese Society for Artificial Intelligence, vol. 21, pp. 20-25.

Williams, B. C. & Nayak, P. P. (1996). A Model-based Approach to Reactive Self-configuring Systems, Proceedings of 13th National Conference on Artificial Intelligence, vol. 2, pp. 971-978.

Yairi, T., Takeishi, N., Oda, T., Nakajima, Y., Nishimura, N., & Takata, N. (2017). A Data-Driven Health Monitoring Method for Satellite Housekeeping Data Based on Probabilistic Clustering and Dimensionality Reduction. IEEE Transactions on Aerospace and Electronic Systems, vol. 53, pp. 1384-1401.

Yairi, T., Fukushima, Y., Liew, C. F., Sakai, Y., & Yamaguchi, Y. (2021). A Data-Driven Approach to Anomaly Detection and Health Monitoring for Artificial Satellites. Advances in Condition Monitoring and Structural Health Monitoring, pp. 129-141.
Special Session Papers