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(yù)測精度，提出一種基于GRA和AHP的廣義回歸神經(jīng)網(wǎng)絡(luò)零件失效概率預(yù)測方法.在分析機械零件失效概率影響因素的基礎(chǔ)上，首先利用灰色關(guān)聯(lián)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(gòu)建機械零件失效概率的評價指標(biāo)層次體系，評估各個指標(biāo)對于零件失效概率的權(quán)重;結(jié)合各個指標(biāo)權(quán)重與初始值，以獲取各指標(biāo)的加權(quán)評價值;最后通過廣義回歸神經(jīng)網(wǎng)絡(luò)（Generalized Regression Neural Network，GRNN）建立以各指標(biāo)加權(quán)評價值來預(yù)測機械零件失效概率的預(yù)測模型.利用本文方法所建立的預(yù)測模型對某企業(yè)數(shù)控轉(zhuǎn)臺的上齒盤失效概率進行預(yù)測，并與傳統(tǒng)的GRNN神經(jīng)網(wǎng)絡(luò)預(yù)測模型、BP神經(jīng)網(wǎng)絡(luò)預(yù)測模型和回歸預(yù)測模型進行對比，結(jié)果顯示本文所建立的模型預(yù)測誤差小于0.8%、殘差在-0.2%～0.2%范圍內(nèi)，均優(yōu)于對比模型的預(yù)測結(jié)果，表明所建立的預(yù)測模型具有更高的精度和更強的穩(wěn)健性，適合于零件失效概率的預(yù)測.

關(guān)鍵詞：廣義回歸神經(jīng)網(wǎng)絡(luò);灰色關(guān)聯(lián)分析;層次分析法;加權(quán)評價值;預(yù)測

中圖分類號：TH165+.4? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?文獻(xiàn)標(biāo)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.