Anomaly detection algorithm for telemetry data of spacecraft based on fusing multiple weighted distribution features

Abstract

Single feature is difficult to achieve a comprehensive description of the complex orbiting spacecraft telemetry data characteristics and is easy to lose telemetry data information, but the statistical distribution operators can fully reflect the characteristics of statistical distribution information. Aiming at the orbiting telemetry data which meets the character of local stability, we propose an anomaly detection algorithm which is based on the characteristics of concentration, discrete characteristic, shape and boundary in the statistical distribution. Four kinds of statistical features are represented by the distribution characteristic operator, and the different distribution features of abnormal patterns are weighted to construct an efficient model for the anomaly detection of multidimensional feature vectors, which can effectively detect the abnormal changes of the local stationary telemetry data of the spacecraft on orbit in the complex space environment. The experiment results of a satellite power system telemetry sequence anomaly detection indicate that the algorithm can effectively detect the change of amplitude, trend and periodic and complex mutation of telemetry data, and the detection effect is robust.