Spacecraft telemetry data anomaly detection method based on ensemble LSTM

Spacecraft is a kind of extraordinary complex systems consisting of integrating structure, thermal control, power, attitude, orbit, and so on. Telemetry data is the only basis to judge the on-orbit spacecraft performance on ground. The effective anomaly detection is a fundamental element to ensure the reliable operation of the spacecraft. In this paper, aiming at the data anomaly detection problems that the telemetry data are the mixture of continuous and discrete samples, and the sample variation is highly correlated with the instructions, a spacecraft telemetry data anomaly detection method is proposed based on ensemble long-short term memory (LSTM) network. The strong nonlinear modeling ability of LSTM is utilized; with matrix norm, the multiple mode mining of tele-control instruction is achieved; through the construction and effective ensemble of the multiple LSTM prediction model, the adaptability of the model to the complicated spacecraft operating condition is improved; then, the anomaly in the telemetry data is effectively labeled. The telemetry data of two kinds of spacecraft from NASA are detected in experiment. The result indicates that the anomaly detection rate of the proposed method is promoted obviously compared with the telemetry data anomaly detection method based on LSTM, the proposed method is especially suitable for contextual anomaly discovery. The test results verify the feasibility of the proposed method, and the study provides an effective data interpretation ability for the ground operation and control of spacecraft. © 2019, Science Press. All right reserved.