Conjunction Detection and Series Clustering of Telemetry Data from Spacecraft in Orbit

Hu Shaolin; Fu Na

doi:doi:10.11648/j.ijdst.20170303.11

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Conjunction Detection and Series Clustering of Telemetry Data from Spacecraft in Orbit

Hu Shaolin, Fu Na

Published in International Journal on Data Science and Technology (Volume 3, Issue 3)

Received: 11 June 2017 Accepted: 29 June 2017 Published: 9 August 2017

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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.

Published in	International Journal on Data Science and Technology (Volume 3, Issue 3)
DOI	10.11648/j.ijdst.20170303.11
Page(s)	34-38
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2017. Published by Science Publishing Group

Keywords

Conjunction Analysis, Stationary Processes, Abnormal Changes

References

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[3]	Jianqing Fan, Qiwei Yao. Nonlinear Time Series—Non- parametric and Parametric Methods. Beijing: Science Press, 2006, 29-274.
[4]	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.
[5]	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.
[6]	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.
[7]	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.
[8]	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.
[9]	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.
[10]	S Cho, SB Kim. One-Class Classification Methods for Process Monitoring and Diagnosis. Intelligent Systems IEEE, 2015, 30(6): 16-18.
[11]	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.

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APA Style

Hu Shaolin, Fu Na. (2017). Conjunction Detection and Series Clustering of Telemetry Data from Spacecraft in Orbit. International Journal on Data Science and Technology, 3(3), 34-38. https://doi.org/10.11648/j.ijdst.20170303.11

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ACS Style

Hu Shaolin; Fu Na. Conjunction Detection and Series Clustering of Telemetry Data from Spacecraft in Orbit. Int. J. Data Sci. Technol. 2017, 3(3), 34-38. doi: 10.11648/j.ijdst.20170303.11

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AMA Style

Hu Shaolin, Fu Na. Conjunction Detection and Series Clustering of Telemetry Data from Spacecraft in Orbit. Int J Data Sci Technol. 2017;3(3):34-38. doi: 10.11648/j.ijdst.20170303.11

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@article{10.11648/j.ijdst.20170303.11,
  author = {Hu Shaolin and Fu Na},
  title = {Conjunction Detection and Series Clustering of Telemetry Data from Spacecraft in Orbit},
  journal = {International Journal on Data Science and Technology},
  volume = {3},
  number = {3},
  pages = {34-38},
  doi = {10.11648/j.ijdst.20170303.11},
  url = {https://doi.org/10.11648/j.ijdst.20170303.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdst.20170303.11},
  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.},
 year = {2017}
}

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TY  - JOUR
T1  - Conjunction Detection and Series Clustering of Telemetry Data from Spacecraft in Orbit
AU  - Hu Shaolin
AU  - Fu Na
Y1  - 2017/08/09
PY  - 2017
N1  - https://doi.org/10.11648/j.ijdst.20170303.11
DO  - 10.11648/j.ijdst.20170303.11
T2  - International Journal on Data Science and Technology
JF  - International Journal on Data Science and Technology
JO  - International Journal on Data Science and Technology
SP  - 34
EP  - 38
PB  - Science Publishing Group
SN  - 2472-2235
UR  - https://doi.org/10.11648/j.ijdst.20170303.11
AB  - 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.
VL  - 3
IS  - 3
ER  -

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Author Information

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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APA Style

Hu Shaolin, Fu Na. (2017). Conjunction Detection and Series Clustering of Telemetry Data from Spacecraft in Orbit. International Journal on Data Science and Technology, 3(3), 34-38. https://doi.org/10.11648/j.ijdst.20170303.11

Copy | Download

ACS Style

Hu Shaolin; Fu Na. Conjunction Detection and Series Clustering of Telemetry Data from Spacecraft in Orbit. Int. J. Data Sci. Technol. 2017, 3(3), 34-38. doi: 10.11648/j.ijdst.20170303.11

Copy | Download

AMA Style

Hu Shaolin, Fu Na. Conjunction Detection and Series Clustering of Telemetry Data from Spacecraft in Orbit. Int J Data Sci Technol. 2017;3(3):34-38. doi: 10.11648/j.ijdst.20170303.11

Copy | Download

@article{10.11648/j.ijdst.20170303.11,
  author = {Hu Shaolin and Fu Na},
  title = {Conjunction Detection and Series Clustering of Telemetry Data from Spacecraft in Orbit},
  journal = {International Journal on Data Science and Technology},
  volume = {3},
  number = {3},
  pages = {34-38},
  doi = {10.11648/j.ijdst.20170303.11},
  url = {https://doi.org/10.11648/j.ijdst.20170303.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdst.20170303.11},
  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.},
 year = {2017}
}

Copy | Download

TY  - JOUR
T1  - Conjunction Detection and Series Clustering of Telemetry Data from Spacecraft in Orbit
AU  - Hu Shaolin
AU  - Fu Na
Y1  - 2017/08/09
PY  - 2017
N1  - https://doi.org/10.11648/j.ijdst.20170303.11
DO  - 10.11648/j.ijdst.20170303.11
T2  - International Journal on Data Science and Technology
JF  - International Journal on Data Science and Technology
JO  - International Journal on Data Science and Technology
SP  - 34
EP  - 38
PB  - Science Publishing Group
SN  - 2472-2235
UR  - https://doi.org/10.11648/j.ijdst.20170303.11
AB  - 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.
VL  - 3
IS  - 3
ER  -

Copy | Download