基于GRA和AHP的GRNN神經網絡零件失效概率預測方法

鞠萍華　柯磊　冉琰　朱曉　李松濤

摘? ?要：為了提高對機械零件失效概率的預測精度，提出一種基于GRA和AHP的廣義回歸神經網絡零件失效概率預測方法.在分析機械零件失效概率影響因素的基礎上，首先利用灰色關聯分析法（Grey Relational Analysis，GRA）分析影響機械零件失效概率的主要因素，通過層次分析法（Analytic Hierarchy Process，AHP）構建機械零件失效概率的評價指標層次體系，評估各個指標對于零件失效概率的權重;結合各個指標權重與初始值，以獲取各指標的加權評價值;最后通過廣義回歸神經網絡（Generalized Regression Neural Network，GRNN）建立以各指標加權評價值來預測機械零件失效概率的預測模型.利用本文方法所建立的預測模型對某企業數控轉臺的上齒盤失效概率進行預測，并與傳統的GRNN神經網絡預測模型、BP神經網絡預測模型和回歸預測模型進行對比，結果顯示本文所建立的模型預測誤差小于0.8%、殘差在-0.2%～0.2%范圍內，均優于對比模型的預測結果，表明所建立的預測模型具有更高的精度和更強的穩健性，適合于零件失效概率的預測.

關鍵詞：廣義回歸神經網絡;灰色關聯分析;層次分析法;加權評價值;預測

中圖分類號：TH165+.4? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?文獻標志碼：A

Failure Probability Prediction Method on Parts of Generalized Regression

Neural Network Based on GRA and AHP

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

（State Key Laboratory of Mechanical Transmissions，Chongqing University，Chongqing 400044，China）

Abstract：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.