Gaussian mixture model based approach to fault detection for satellite communication equipment

Aiming at the problem of fault detection for satellite communication system, a prediction method based on Gaussian mixture model is proposed. Firstly, the observation sequence is collected by modem as well as frequency conversion equipment. Then feature parameters are extracted after pre-processing. The expectation maximum algorithm is applied to train the Gaussian mixture model. The posterior probabilities of the feature sequence separately relative to the normal and abnormal models are calculated by the maximum a posteriori probability criterion. In order to determine the right working state, the model corresponding to the larger probability is selected. Since the result of the judgment is presented in the form of probability, it is necessary to choose the threshold value after several times. Finally, the performance and average prediction accuracy of the prediction model are tested by simulation.