Failure Probability Prediction Method on Parts of Generalized Regression Neural Network Based on GRA and AHP

JU Pinghua, DE Lei, RAN Yan, ZHU Xiao, LI Songtao


To improve the prediction precision of failure probability of machine parts,failure probability prediction method of generalized regression neural network based on GRA and AHP was proposed. The main influence factors on failure probability of mechanical parts were analyzed by grey relational analysis method based on the analysis of influence factors on failure probability of mechanical parts. The hierarchy model of evaluation index for failure probability of each mechanical part was constructed and the weight of each index was evaluated by analytic hierarchy process. Then,the weight and initial value of each index were combined to obtain the weighted evaluation value of each index. Finally,the generalized regression neural network was used to establish a predictive model by using weighted evaluation value of each index to predict the failure probability of mechanical parts. This optimization method was applied to predict the failure probability of upper gear disk in numerical control rotary table. The prediction results of traditional generalized regression neural network ,BP neural network and regression analysis method were compared. The result shows that the prediction error of the proposed model is less than 0.8%,and the residual error is in the range of -0.2% and 0.2%,which is better than the comparison models. Meanwhile,the model established by using the proposed method in this paper has higher accuracy and stronger stability,which is suitable for the prediction of failure probability of parts.



Keywords: generalized regression neural network,  gray relational analysis,  analytic hierarchy process,  weighted evaluation value,  prediction

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