Diagnosis method for spacecraft using dynamic bayesian networks

Yoshinobu Kawahara, Takehisa Yairi, Kazuo Machida

研究成果: Contribution to journalConference article査読

2 被引用数 (Scopus)

抄録

Development of sophisticated anomaly detection and diagnosis methods for spacecraft is one of the important problems in space system operation. In this study, we propose a diagnosis method for spacecraft using probabilistic reasoning and statistical learning with Dynamic Bayesian Networks (DBNs). In this method, the DBNs are initially from prior-knowledge, then modified or partly re-constructed by statistical learning with operation data, as a result adaptable and in-depth diagnosis is performed by probabilistic reasoning using the DBNs. The proposed method was applied to the telemetry data that simulates the malfunction of thrusters in rendezvous maneuver of spacecraft, and the effectiveness of the method was confirmed.

本文言語英語
ページ(範囲)649-656
ページ数8
ジャーナルEuropean Space Agency, (Special Publication) ESA SP
603
出版ステータス出版済み - 2005
外部発表はい
イベントi- SAIRAS 2005 - The 8th International Symposium on Artificial Intelligence, Robotics and Automation in Space - Munich, ドイツ
継続期間: 9 5 20059 8 2005

All Science Journal Classification (ASJC) codes

  • Aerospace Engineering
  • Space and Planetary Science

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