Abstract:

In order to ensure the stable operation of a satellite and prolong its life, an anomaly detection method based on angle deviation(ADMAD) is proposed. In the high-dimensional data space of the satellite telemetry data, the method applies the shared nearest neighbors (SNN) algorithm to construct the reference point sets. Then the method selects the feature attributes associated with the anomaly by applying a method based on angle deviation using angle replacing distance.Finally, the normalized Mahalanobis distance is used to calculate the anomaly scores of points. Combining with the statistical knowledge, the threshold based on the anomaly scores is obtained, and the data sets are classified. We verified the proposed method using the telemetry data in control and power subsystem of a satellite in July to September, 2014 and July to September, 2015 respectively. The experimental results indicate that the accuracy of the proposed algorithm could reach more than 95% under the condition of lack of the field knowledge.The robustness of the proposed algorithm is higher. Simultaneously, it can detect the anomaly of satellite subsystem timely and effectively.

Key words: Satellite subsystem, Angle deviation, Attribute selection, Anomaly detection