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By Yoshinobu Kawahara, Takehisa Yairi and Kazuo Machida


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 priorknowledge, 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

Topics: Key words, Diagnosis, Fault Detection, Dynamic Bayesian Networks, Probabilistic Reasoning, Statistical Learning
Year: 2009
OAI identifier: oai:CiteSeerX.psu:
Provided by: CiteSeerX
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