LAUSR

To ensure the safety and stability of spacecrafts of which thousands of telemetry parameters are monitored, fast and accurate response to anomalies or potential hazards is very important and challenging. This task becomes more difficult when the obtained telemetry data are sampled at irregular intervals. Long Short-Term Memory networks (LSTM), as time series prediction models, have been applied to satellite anomaly detection and show a promising prospect. However, the anomaly detection method merely based on LSTM does not show a stable performance: when the prediction performance of LSTM is not satisfying, the performance of subsequent anomaly detection will be affected, and the impact is augmented when the telemetry data are of irregular intervals. In order to solve these problems, time intervals are introduced into the LSTM model directly. Besides that, a novel anomaly detection method, Detecting Anomalies using LSTM and Ensembled One-Class Support Vector Machines (DALEO) is proposed to further improve the performance of anomaly detection. In DALEO, multiple One-Class Support Vector Machines are used to obtain the ensemble outputs of high precision and high recall respectively. These ensemble outputs are integrated into the two stages of the anomaly detection method with LSTM in a novel way. Extensive empirical studies on real-world datasets of satellites and space shuttles demonstrate that DALEO improves the performance of anomaly detection significantly when dealing with telemetry data with irregular intervals.