Spacecraft intelligent anomaly detection method supporting multi-source mixed heterogeneous data types

Abstract

The application discloses a spacecraft intelligent anomaly detection method supporting multi-source mixed heterogeneous data types, first, for discrete data, the discrete data dictionary is used to detect data anomalies by using residual reconstruction; then, for continuous data, the analysis result of discrete data is used to prune the search space to detect anomalies; finally, for mixed type data, the above two methods are combined to adjust the algorithm optimization target, and fast and accurate detection of anomalies is realized. Compared with the traditional method, the application patent fully explores the relationship between data sparse representation and abnormal characteristics, and establishes an abnormal detection method supporting discrete signals, aiming at the discrete data processing needs of the autonomous health management and intelligent precise operation and maintenance scene of the spacecraft. In addition, based on the idea of discrete and continuous data fusion, the application designs an abnormal detection algorithm based on sparse representation, which supports the simultaneous detection of abnormal characteristics of two types of heterogeneous data.

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