Fluctuation Feature Extraction of Satellite Telemetry Data and On-Orbit Anomaly Detection

On-orbit anomaly detection is an open problem for long-term management of satellites, in which defining and extracting effective features based on satellite telemetry data is one of the key points. Classical spectral analytic methods such as Fourier analysis, Wavelet analysis methods and other signal processing methods have make contributions to the cognition and management of satellite telemetry data. However, with satellite running on orbit and huge data accumulated, it is difficult to utilize and cognize the telemetry data features due to the discrete values, huge volumes, containing large noise, loss of data and complex anomaly, which makes the features of telemetry data non-significant and hinders the anomaly detection of telemetry data. This paper proposes a set of fluctuation feature of satellite telemetry data, called state-counting method (SCM), in which the changing frequency and amplitude of satellite telemetry data are extracted to describe the fluctuation features of satellite telemetry data. This extraction method is feasible and efficient, and is not sensitive to noise and outliers in the telemetry data. Based on the fluctuation features, an efficient anomaly detection method based on SPRT is proposed. Comparison of the approach with others shows that the fluctuation features proposed in this article can be used to recognize the normal and anomaly satellite states. From the index system of scoring, this approach has high computational efficiency and better detection performance.