Conjunction Detection and Series Clustering of Telemetry Data from Spacecraft in Orbit
International Journal on Data Science and Technology
Volume 3, Issue 3, May 2017, Pages: 34-38
Received: Jun. 11, 2017; Accepted: Jun. 29, 2017; Published: Aug. 9, 2017
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Authors
Hu Shaolin, School of Automation, Foshan University, Foshan, China
Fu Na, Key Laboratory of Fault Diagnosis & Maintenance of Spacecraft in Orbit, Xi’an, China
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Abstract
To explore the conjunction of abnormal changes among different processes is a key and challenging technique problem in processes monitoring, in faults analysis, and in faults location. In this paper, an indication series is used to symbolize the abnormal change in sampling series, two kinds of conjunction test indices are constructed to measure the conjunction degrees, which rely on the indication series of multidimensional synchronization sampling series and the abnormal change percentage series of multidimensional asynchronous sampling series separately. What is more, these conjunction-test indices are successfully used to set up the clustering algorithms of abnormal changes in multidimensional series. Some Monte Carlo results show that algorithms given in this paper are efficient. The idea and technological methods of this paper are helpful for us to get viable approaches to analyze abnormal changes and to diagnose faults in large-scale dynamic system.
Keywords
Conjunction Analysis, Stationary Processes, Abnormal Changes
To cite this article
Hu Shaolin, Fu Na, Conjunction Detection and Series Clustering of Telemetry Data from Spacecraft in Orbit, International Journal on Data Science and Technology. Vol. 3, No. 3, 2017, pp. 34-38. doi: 10.11648/j.ijdst.20170303.11
Copyright
Copyright © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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