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 Spacecraft Telemetry Monitoring Method Based on Dimensionality Reduction and Clustering

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抄録

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.

収録刊行物

  • 日本航空宇宙学会論文集 = Journal of the Japan Society for Aeronautical and Space Sciences

    日本航空宇宙学会論文集 = Journal of the Japan Society for Aeronautical and Space Sciences 59(691), 197-205, 2011-08-05

    一般社団法人 日本航空宇宙学会

参考文献:  15件中 1-15件 を表示

被引用文献:  2件中 1-2件 を表示

各種コード

  • NII論文ID(NAID)
    10030274798
  • NII書誌ID(NCID)
    AA11307372
  • 本文言語コード
    JPN
  • 資料種別
    ART
  • ISSN
    13446460
  • NDL 記事登録ID
    11212334
  • NDL 雑誌分類
    ZN25(科学技術--運輸工学--航空機・ロケット)
  • NDL 請求記号
    Z74-B503
  • データ提供元
    CJP書誌  CJP引用  NDL  J-STAGE 
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