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

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.