Fault diagnosis for spacecraft using probabilistic reasoning and statistical learning with dynamic bayesian networks

Yoshinobu Kawahara, Takehisa Yairi, Kazuo Machida

Research output: Contribution to conferencePaper

1 Citation (Scopus)

Abstract

The development of sophisticated anomaly detection and diagnosis methods for spacecraft is one of the important problems in space system operation. Many researches have been carried on this problem, however these depend only on specific resource; knowledge or data. In this paper, we propose a diagnosis method for spacecraft using Dynamic Bayesian Networks which uses both prior- knowledge and data in a natural way in model-acquisition and perform adaptable and in-depth diagnosis by probabilistic reasoning. The proposed method was applied to the artificial telemetry data that simulates the malfunction of thrusters in rendezvous maneuver of spacecraft, and the effectiveness of the method was confirmed.

Original languageEnglish
Pages5823-5830
Number of pages8
Publication statusPublished - Dec 1 2005
Externally publishedYes
EventInternational Astronautical Federation - 56th International Astronautical Congress 2005 - Fukuoka, Japan
Duration: Oct 17 2005Oct 21 2005

Other

OtherInternational Astronautical Federation - 56th International Astronautical Congress 2005
CountryJapan
CityFukuoka
Period10/17/0510/21/05

All Science Journal Classification (ASJC) codes

  • Space and Planetary Science
  • Aerospace Engineering

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    Kawahara, Y., Yairi, T., & Machida, K. (2005). Fault diagnosis for spacecraft using probabilistic reasoning and statistical learning with dynamic bayesian networks. 5823-5830. Paper presented at International Astronautical Federation - 56th International Astronautical Congress 2005, Fukuoka, Japan.