日本航空宇宙学会論文集
Online ISSN : 2432-3691
Print ISSN : 1344-6460
論文
次元削減とクラスタリングによる宇宙機テレメトリ監視法
矢入 健久乾 稔河原 吉伸高田 昇
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ジャーナル フリー

2011 年 59 巻 691 号 p. 197-205

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Development of intelligent system monitoring and fault detection techniques for spacecraft is of a great interest in the space engineering. In this paper, we propose a “data-driven” anomaly detection framework for spacecraft telemetry data using dimensionality reduction and clustering techniques. In this framework, we first apply dimensionality reduction or/and clustering algorithms to a normal training data set, so that we obtain statistical models representing the normal behavior of spacecraft. After the training, we monitor test data sets and detect anomaly if any, by using the obtained models. This framework is so comprehensive that a variety of clustering, dimensionality reduction and their hybrid algorithms can be used with it. In the experiment, we tested several algorithms on the past artificial satellite data, and found that a hybrid method called VQPCA is more suitable for modeling high-dimensional and multi-modal telemetry than others.

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© 2011 The Japan Society for Aeronautical and Space Sciences
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