Spacecraft Diagnosis Method Using Dynamic Bayesian Networks
Spacecraft Diagnosis Method Using Dynamic Bayesian Networks
复制标题
基于动态贝叶斯网络的航天器诊断方法
DOI：
10.1527/tjsai.21.45
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发表时间：
2006
影响因子：
--
通讯作者：
K. Machida
中科院分区：
文献类型：
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作者：
Y. Kawahara;T. Yairi;K. Machida
发展先进的航天器异常检测与诊断方法是航天系统运行中的重要问题之一。在这项研究中，我们提出了一种诊断方法，航天器使用概率推理和统计学习与动态贝叶斯网络（DBNs）。在该方法中，DBN最初从先验知识中创建，然后通过从操作数据中的统计学习来修改或部分重构，从而通过使用DBN的概率推理来执行适应性和深入的诊断。该方法以自然的方式融合和使用知识和数据，具有基于知识和数据驱动两极方法的能力。将该方法应用于模拟航天器交会机动时推力器故障的遥测数据，验证了该方法的有效性。
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 created from prior knowledge, then modified or partly re-constructed by statistical learning from operation data, as a result adaptable and in-depth diagnosis is performed by probabilistic reasoning using the DBNs. This method fuses and uses both knowledge and data in a natural way and has the both ability which two polar approaches; knowledge-based and data-driven have. The proposed method was applied to the telemetry data that simulates malfunction of thrusters in rendezvous maneuver of spacecraft, and the effectiveness of the method was confirmed.
DOI：
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发表时间：
2018
期刊：
影响因子：
--
作者：
石井 晶;矢田 和善;青嶋 誠;町田由登，フンドックトゥアン;Stephen Wu
通讯作者：
Stephen Wu