An anomaly detection method for satellite based on correlation probability model

doi:10.7523/j.issn.2095-6134.2021.03.015

Abstract

Abstract: During the orbital operation of the satellite, the telemetry data is an important basis for reflecting the health status of satellites. The detection of potential anomalies in telemetry data is of great significance for the maintenance of satellites. Threshold method used in engineering can not effectively detect failure symptoms within the threshold, and the current theoretical research in this field can not effectively tap the potential correlation of multidimensional telemetry sequences. Therefore, this paper, taking the actual on-orbit telemetry data of a satellite as the object, adopts a detection method of correlation probability model that incorporates principal component analysis (PCA), and analyzes the failure case deeply. It verifies that this method can detect satellite's early failure and the results are compared and analyzed. This method can also quickly diagnose the failure so that the ground can handle it in time to avoid further accidents.

Key words: anomaly detection, failure symptoms, correlation probability model, PCA detection

CLC Number:

TN911.71

SUN Yuhao, LI Guotong, ZHANG Ge. An anomaly detection method for satellite based on correlation probability model[J]. Journal of University of Chinese Academy of Sciences, 2021, 38(3): 409-416.

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