An anomaly detection method for spacecraft solar arrays based on the ILS-SVM model

Solar arrays are important and indispensable parts of spacecraft and provide energy support for spacecraft to operate in orbit and complete on-orbit missions. When a spacecraft is in orbit,because the solar array is exposed to the harsh space environment,with increasing working time,the performance of its internal electronic components gradually degrade until abnor-mal damage occurs. This damage makes solar array power gene-ration unable to fully meet the energy demand of a spacecraft. Therefore,timely and accurate detection of solar array anoma-lies is of great significance for the on-orbit operation and mainte-nance management of spacecraft. In this paper,we propose an anomaly detection method for spacecraft solar arrays based on the integrated least squares support vector machine (ILS-SVM) model: it selects correlated telemetry data from spacecraft solar arrays to form a training set and extracts n groups of training subsets from this set,then gets n corresponding least squares support vector machine (LS-SVM) submodels by training on these training subsets,respectively; after that,the ILS-SVM model is obtained by integrating these submodels through a weighting operation to increase the prediction accuracy and so on; finally,based on the obtained ILS-SVM model,a parameter-free and unsupervised anomaly determination method is pro-posed to detect the health status of solar arrays. We use the telemetry data set from a satellite in orbit to carry out experimen-tal verification and find that the proposed method can diagnose solar array anomalies in time and can capture the signs before a solar array anomaly occurs,which reflects the applicability of the method.