Multivariate Engineering Parameter Anomaly Detection of the Satellite Based on Similarity Metric of the Symbolic Representation

The complexity of the satellite system is increasing, and the comprehensive analysis of the abnormal correlation between the satellite with multiple parameters is important for the safe operation of the satellite and the correct execution of space tasks. Aiming at the characteristics of large amount of engineering parameter data, high parameter correlation and pseudo-period, a satellite multivariate engineering parameter anomaly detection method based on symbolized representation similarity measurement is proposed. Using real satellite engineering parameter data, based on Symbol Aggregation Algorithm (SAX), this paper studies the anomaly detection problem of satellite multiple engineering parameters, solves the problem of the context information, realizes effective fusion, and forms an optimized anomaly detection algorithm based on similarity measurement Fast-DTW algorithm. Results show that during the abnormality detection process, the recall, precision and F1 score are 0.947, 0.9 and 0.923 respectively in the real satellite power subsystem, and the algorithm can be actually used in satellite anomaly detection to improve the safety of satellite in-orbit operation.