Life prediction of high-reliability product based on optimizedgrey theory
In the life prediction of high reliability products,the traditional acceleration model is blamed for its low fitting accuracy and large prediction error,so it is rahter difficult to make a more accurate life prediction for products.In response to this,a new method based on optimized grey prediction model is proposed to modify the parameters of the traditional accelera-tion model so as to improve the significance and prediction accuracy of the acceleration model.Firstly,the dynamic optimiza-tion method is used to determine the optimal background value and reduce the fitting error of the grey prediction model.Then,the optimized grey model is used to predict the life of the product at near normal service temperature,and the failure data is ef-fectively expanded in the same dimension.The expanded data is used to correct the parameters of the traditional acceleration model and test its significance.Finally,the example analysis shows that the modified model has higher significance and smaller prediction relative error,and the prediction accuracy is higher at the temperature closer to the normal use of the product,indi-cating that this method has the characteristics of less data,high prediction accuracy and strong practicability.Therefore,it is feasible and more effective to use this method to predict the life of high reliability products.
accelerated life testgrey prediction modellife predictiondynamic optimization methodhigh reliability
万方数据
维普
