State Trend Prediction of Spacecraft Using PSO-SVR

Fault prediction is the core content and crucial technology for health monitoring of the in-orbit spacecraft, and predicting the future trend of telemetry data is the prerequisite and basis for fault prediction. This paper presents a state trend prediction method for spacecraft based on particle swarm optimization (PSO) and support vector regression (SVR). The method applies SVR to construct a regression prediction model of telemetry data. SVR is a learning procedure based on statistical learning theory, which employs the training data to build an excellent prediction model in the situations of small sample. The complexity and generalization performance of the SVR model is influenced by its training parameters. In this paper, PSO is applied to optimize the parameters of SVR model. The results show that the method is efficient and practical for telemetry data prediction of the in-orbit spacecraft.