关键词: BP神经网络, 多几何要素, 灰关联分析, 预测, 装配产品, 多种失效模式, 概率分布特征, 机械零部件, 失效行为, 寿命预测

Abstract: In the industrial production, assembled product with many parts usually has the feature of multiple system characteristics and multiple geometric elements effect. To get the exactly math model of the system so as to predict the system characteristics are essentially important for the manufacture process. Because of complexity of the assembled product system with multiple geometric elements and different effect degree, building the BP neural network forecasting model of the system directly along with increment of input neurons and hidden layer neurons leads to very complicated structure of neutral network, increase of study and training time, slow convergence rate, and low precision forecasting. A new method is proposed to build the forecasting model. After analyzing the multiple geometric elements of the system, the grey correlation model is used to obtain the main geometric elements. Then the main geometric elements are used to built the BP neural network and simplify the BP neural network model. The model can truly reflect the feature of the system and can achieve high-precision forecasting for the assembled product system characteristics with multiple geometric elements. In this way, the characteristics predicting of hydraulic valve system is achieved. The hydraulic valve, an assembled product with multiple geometric elements, is taken as example. Through the study on the correlation degree of multiple geometric elements of the hydraulic valve system, the main geometric elements that influence the system are used as input to build a simplified forecasting model of BP neural network. Experimental results indicate that the forecasting model features simple structure, quick convergence and high-precision forecasting.*

Key words: Assembled product, BP neural network, Forecasting, Grey correlation model, Multiple geometric elements, failure behavior, life prediction, multiple failure modes, probability distribution characteristic, Mechanical components