Application research of grey prediction model in rocket launcher fault prediction

Abstract

This paper puts forward the importance of artillery equipment failure prediction, introduces the basic principles of grey prediction modeling, and finds the traditional GM (grey model) (1,1)deficiency. Aiming at this deficiency, the GM(1,1) model is improved based on residual correction and adaptive learning. The fault prediction of a rocket launcher automatic leveling system is taken as an application example, and the grey prediction GM(1,1) is compared and analyzed. Basic model and improved prediction of GM(1,1) model. The analysis shows that the improved GM(1, 1) grey prediction method improves the prediction accuracy and achieves a faster convergence speed, which indicates that the model is feasible and effective, and can provide an effective technical basis for the rocket gun automatic leveling system fault prediction.