Spacecraft Telemetry Monitoring Method Based on Dimensionality Reduction and Clustering

DOI Web Site Web Site 6 Citations 18 References

Bibliographic Information

Other Title
  • 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
  • ジゲン サクゲン ト クラスタリング ニ ヨル ウチュウキ テレメトリ カンシホウ

Search this article

Abstract

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.

Journal

Citations (6)*help

See more

References(18)*help

See more

Related Projects

See more

Keywords

Details 詳細情報について

Report a problem

Back to top