Abstract
In the operation of artificial satellites, overall health monitoring of the system and detection of symptoms of potential troubles are very important. For about twenty years, we have studied the data-driven health monitoring methods for artificial satellites, in which machine learning techniques are applied to the house-keeping data of satellites. In this paper, we review the basic of anomaly detection based on unsupervised learning and some results in early studies and then discuss the lessons learned and challenges to be tackled in the data-driven health monitoring for artificial satellites.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Tallo DP, Durkin J, Petrik EJ (1992) Intelligent fault isolation and diagnosis for communication satellite systems. Telematics Inf 9(3/4):173–190
Williams BC, Nayak PP (1996) A model-based approach to reactive self-configuring systems. In: Proceedings of the 13th national conference on artificial intelligence, pp 971–978
DeCoste D (1997) Automated learning and monitoring of limit functions. In: Proceedings international symposium on artificial intelligence, robotics, and automation in space, pp 287–292
Schwabacher M, Oza N, Matthews B (2009) Unsupervised anomaly detection for liquid-fueled rocket propulsion health monitoring. J Aerosp Comput Inf Commun 6(7):464–482
Iverson DL et al (2012) General purpose data-driven monitoring for space operations. J Aerosp Comput Inf Commun 9(2):26–44
Fujimaki R, Yairi T, Machida K (2005) An approach to spacecraft anomaly detection problem using kernel feature space. In: Proceeding of the 11th ACM SIGKDD international conference on knowledge discovery in data mining, pp 401–410
Inui M, Kawahara Y, Goto K, Yairi T, Machida K (2009) Adaptive limit checking for spacecraft telemetry data using kernel principal component analysis. Trans Japan Soc Aeronaut Space Sci Space Technol Japan 7(ists26):Pf-11–Pf-16
Yairi T et al (2010) Spacecraft telemetry monitoring by dimensionality reduction and reconstru ction. In: The NASA conference on intelligent data understanding
Yairi T et al (2017) A data-driven health monitoring method for satellite housekeeping data based on probabilistic clustering and dimensionality reduction. IEEE Trans Aerosp Electron Syst 53(3):1–18
Acknowledgements
This work was supported by JST START Grant Number JPMJST1814, Japan. Some results in this paper are from our past studies conducted under the support of JSPS KAKENHI Grant Numbers 26289320.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
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. In: Gelman, L., Martin, N., Malcolm, A.A., (Edmund) Liew, C.K. (eds) Advances in Condition Monitoring and Structural Health Monitoring. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-9199-0_13
Download citation
DOI: https://doi.org/10.1007/978-981-15-9199-0_13
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-9198-3
Online ISBN: 978-981-15-9199-0
eBook Packages: EngineeringEngineering (R0)