Outlier-tolerant K Charts Based Tendency Analysis with Variable Fineness of Big Telemetry Data

DOI

10.2991/isrme-15.2015.123How to use a DOI?

Keywords

Big data, Telemetry data, K Charts analysis

Abstract

The spacecraft telemetry data is the only way for operation managers to understand the spacecraft’s real working state in space. In face with the increasing amount of telemetry data growing day by day, it is hard to overcome the long data time span, large data volume, dirty data and etc. It is very necessary to explore some effective analytical methods to analysis the big telemetry data. In this paper, the K Charts analysis method is referenced and used to deal with the telemetry big data for safety analysis. Using the transformation and improvement, particle size analysis technique, the fault-tolerant K Charts is proposed and established for spacecraft telemetry data change analysis. K Charts cycle through the transforming of variables, different time granularity, formation, K Charts combination, to realize mass spacecraft telemetry sampling data association analysis of structure the shape and the state. The results of application show that, combinations of different sizes of K Charts can reflect the law of the data intrinsic change more accurately than ordinary timing diagrams.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Bo Qin
AU  - Shaolin Hu
AU  - Yumin Yang
AU  - Xiaohong Guo
PY  - 2015/04
DA  - 2015/04
TI  - Outlier-tolerant K Charts Based Tendency Analysis with Variable Fineness of Big Telemetry Data
BT  - Proceedings of the 2015 International Conference on Intelligent Systems Research and Mechatronics Engineering
PB  - Atlantis Press
SP  - 601
EP  - 607
SN  - 1951-6851
UR  - https://doi.org/10.2991/isrme-15.2015.123
DO  - 10.2991/isrme-15.2015.123
ID  - Qin2015/04
ER  -