Visualization Methods for Spacecraft Telemetry Data Using Change-point Detection and Clustering

Abstract

For secure operation of spacecraft, automatic or assistive health monitoring systems utilizing telemetry data are important. However, it is difficult to utilize them comprehensively because they consist of myriad heterogeneous variables. Although various monitoring systems focusing on only a few variables or homogeneous variables have been suggested, a definitive method to deal with the relationship among multiple heterogeneous variables has not yet. This paper proposes a new visualization framework that aims to show the correlation rules underlying multiple variables of spacecraft telemetry data. The proposed framework consists of a change-point detection algorithm based on subspace identification, clustering methods using dimensionality reduction, and a visualization method using heatmaps. In experiments conducted with real telemetry data obtained from JAXA spacecraft SDS-4, the proposed framework demonstrated effective visualizations that reflected the correlations among variables expected from mechanical characteristics of the satellite. Despite differences in scales and/or units, this framework succeeded in visualizing dynamic correlations not only among continuous variables but also among continuous and discrete variables. This framework can be utilized as an initial stage of anomaly detection focusing on the relationship among multiple variables, as well as a method to perceive the overall state of the spacecraft at a glance.