A Data-driven Model for Satellite Health State Interpretation
In response to the problems faced by the processing of massive satellite health manage-ment data,such as high parameter dimensions,multiple redundant parameters,difficulty in quan-tifying the relationship between parameters,and difficulty in quantitatively interpreting the health status,a data processing mechanism combing correlation cluster analysis and a health state inter-pretation model integrating multi-classifier integration is proposed in this paper.Firstly,the maxi-mum information coefficient(MIC)is used to quantify the parameter relationships and the key feature parameters are selected.Then the key feature parameter data is converted into the health status knowledge database by cluster analysis.Finally,multiple classifiers are trained based on the health knowledge database to monitor the satellite health status in real-time.The validity of the model is verified using the telemetry data from a certain satellite payload.The simulation results show that the model trained with key associated parameters after data mining,has good ability to detect satellite abnormal state with an accuracy of 98%it can provide reference for the selection of on-orbit satellite health monitoring means.
maximum information coefficientfeature selectionclusteringmulti-classifier integra-tionanomaly detection
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