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
Views 739      Downloads 34
Hu Shaolin, School of Automation, Foshan University, Foshan, China
Fu Na, Key Laboratory of Fault Diagnosis & Maintenance of Spacecraft in Orbit, Xi’an, China
Article Tools
Follow on us
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.
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 © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
B L Bowerman, R T O’Connell, A B Koehler. Forecasting, Time Series, and Regression: an Applied Approach, 2004, 1-62.
S Menard. Applied logistic regression analysis. Shanghai People’s Press, 2002: 2002, 1-139.
Jianqing Fan, Qiwei Yao. Nonlinear Time Series—Non- parametric and Parametric Methods. Beijing: Science Press, 2006, 29-274.
Hu Shaolin, Li Ye, Zhang Dong. Fault-tolerant mining algorithm of sampling data from dynamic system. 25th Chinese control and decision conference. 2013, pp. 4927-4931.
Hu Shaolin, Li Ye, Zhang Wei. Algorithms of Data Mining and Knowledge Discovery of Correlativity in two-dimensional Time Series. Applied Mechanics and Materials, 2013, Vols. 263-266 pp 1844-1848.
S. P. Bingulac. “On the compatibility of adaptive controllers (Published Conference Proceedings style)” in Proc. 4th Annu. Allerton Conf. Circuits and Systems Theory, New York, 1994, pp. 8–16.
J Vega, S Dormido-Canto, T Cruz, et al. Real-time change detection in data streams with FPGAs. Fusion Engineering & Design, 2014, 89 (5): 644-648.
C Svärd, M Nyberg, E Frisk, et al. Data-driven and adaptive statistical residual evaluation for fault detection with an automotive application. Mechanical Systems & Signal Processing, 2014, 45(1): 170-192.
SX Ding, P Zhang, T Jeinsch, et al. A survey of the application of basic data-driven and model-based methods in process monitoring and fault diagnosis. IFAC Proceedings Volumes, 2011, 44(1): 12380-12388.
S Cho, SB Kim. One-Class Classification Methods for Process Monitoring and Diagnosis. Intelligent Systems IEEE, 2015, 30(6): 16-18.
Chai Min, Yng Yue, Xu Xiaohui, et al. The Dimension Reduction Analysis of Spacecraft’s Telemetry Data for Fault Diagnosis. Journal of Projectiles, Rockets, Missiles and Guidance, 2014, 34(1): 150-153.
Science Publishing Group
NEW YORK, NY 10018
Tel: (001)347-688-8931