The DOI matches its Crossref record; 23 of the 26 references it cites were found in a registry or an index.
A Data-Driven Health Monitoring Method for Satellite Housekeeping Data Based on Probabilistic Clustering and Dimensionality Reduction
Open accessAbstract
In the operation of artificial satellites, it is very important to monitor the health status of the systems and detect any symptoms of anomalies in the housekeeping data as soon as possible. Recently, the data-driven approach to the system monitoring problem, in which statistical machine learning techniques are applied to the large amount of measurement data collected in the past, has attracted considerable attention. In this paper, we propose a new data-driven health monitoring and anomaly detection method for artificial satellites based on probabilistic dimensionality reduction and clustering, taking into consideration the miscellaneous characteristics of the spacecraft housekeeping data. We applied our method to the telemetry data of the small demonstration satellite 4 (SDS-4) of the Japan Aerospace Exploration Agency (JAXA) and evaluated its effectiveness. The results show that the proposed system provides satellite operators with valuable information for understanding the health status of the system and inferring the causes of anomalies.
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“Given the need to overcome the limitations of limit checking techniques some rule-based expert systems and model-based reasoning methods were developed [130].”
From Applications and Challenges of Artificial Intelligence in Space Missions (2024) · cites this paper as [130]
Citations
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Cited by
Sorted by citations
DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection
Yang, Y., Zhang, C., Zhou, T., Wen, Q., & Sun, L. (2023) · Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining · doi:10.1145/3580305.3599295 · Cited by 387
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Imputation-based Time-Series Anomaly Detection with Conditional Weight-Incremental Diffusion Models
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Metadata
- Authors
- Yairi, T., Takeishi, N., Oda, T., Nakajima, Y., Nishimura, N., & Takata, N.
- Affiliations
- The University of Tokyo (JP)
- Japan Aerospace Exploration Agency (JP)
- Venue
- IEEE Transactions on Aerospace and Electronic Systems
- Published
- 2017-06
- Volume
- 53
- Issue
- 3
- Pages
- 1384-1401
- ISSN
- 0018-9251 (ISSN-L)
- OpenAlex
- W2593048559
- Cited by
- 185
- References
- 26
- Citations by year
- 2026: 18 · 2025: 33 · 2024: 30 · 2023: 38 · 2022: 16 · 2021: 17 · 2020: 12 · 2019: 17 · 2018: 4
- FWCI
- 5.67
- Type
- journal article
- Language
- en
- Open access
- Locations
- IEEE Transactions on Aerospace and Electronic Systems (publishedVersion)
- JAXA Repository (JAXA) (submittedVersion, cc-by-nc-nd)
- Funders
- JSPS KAKENHI (JP22560779, JP26289320)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Engineering › Aerospace Engineering › Spacecraft Design and Technology
- Keywords
- satellite health monitoring, housekeeping data, anomaly detection, probabilistic clustering, spacecraft telemetry, data-driven monitoring
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