Anomaly Detection and Identification in Satellite Telemetry Data Based on Pseudo-Period

Featured Application The method proposed in this paper takes the advantage of pseudo-period to process the massive telemetry data for precise anomaly detection and identification. It can be mainly applied to the fault diagnosis, fault detection and health management in some specific complex control systems such as the industrial application scene. To effectively detect and identify the anomaly data in massive satellite telemetry data sets, the novel detection and identification method based on the pseudo-period was proposed in this paper. First, the raw data were compressed by extracting the shape salient points. Second, the compressed data were symbolized by the tilt angle of the adjacent data points. Based on this symbolization, the pseudo-period of the data was extracted. Third, the phase-plane trajectories corresponding to the pseudo-period data were obtained by using the pseudo-period as the basic analytical unit, and then, the phase-plane was divided into statistical regions. Finally, anomaly detection and identification of the raw data were achieved by analyzing the statistical values of the phase-plane trajectory points in each partition region. This method was verified by a simulation test that used the measured data of the satellite momentum wheel rotation. The simulation results showed that the proposed method could achieve the pseudo-period extraction of the measured data and the detection and identification of the anomalous telemetry data.