Study on communication system fault prediction based on multivariate grey model

Abstract

Communication system failures often occur and it is difficult to find a certain pattern, this paper addresses the difficulty of fault prediction in electronic information systems represented by communication systems. Considering the characteristics of communication systems, such as complex structure, high integration, working principle and fault mechanism, this paper proposes to use the Multivariate Growth Model (MGM) for fault prediction based on the data, utilizing the Grey System Theory. The proposed model covers multiple characteristic variables causing communication system faults, monitors and predicts the characteristic variables in the operation of the communication system based on time series, and takes the predicted values as the basis for fault prediction. A certain type of communication system RF module is taken as case study to analyze the performance of the proposed fault prediction model. Through the analysis of the failure mechanism and the actual failure cases of the RF module of the communication system, four characteristic parameters, frequency error, medium power, squelch sensitivity and maximum audio level, are selected The comparative analysis shows that the proposed MGM method has better prediction accuracy than the traditional mean GM (1, 1) model. Besides, the model can process and calculate data with the help of computer software, thus improving the application efficiency of multivariate MGM (1, m) prediction model. The method proposed in this paper provides a feasible solution for predicting the critical faults of complex communication systems.