The Cutter Of The Coal Mining Machine Life Prediction Based On SVM

Cut teeth is a part which consume more,damage easily and replace frequently,thereplacement time is from eight hours to thirty-five hours,correct forecast its life can improve theproduction efficiency and increase safety.The traditional cut teeth fatigue analysis is based onfatigue life theory,use finite element analysis software to carry on the analysis,it needs toconsider a number of factors,and many of the material fatigue parameters are not easy to get.This thesis in use the forecast fatigue life of the cut teeth as the starting point, put forward a kindof new method to analyse the fatigue life of cut teeth--support vector machine (SVM).Basing on support vector machine as foundation,through the support vector machine (SVM)to coal mine cut teeth fatigue log learning, realize the prediction of fatigue life, and cut the lowercost.In this paper, to the datong phillips mining machinery company a certain type of cut teethproceed to structural analysis,using ANSYS general post-processing fatigue analysis modulegenerated fatigue data, approximate replace real data,through analysing the forecast results checkthe feasibility of forecast cut teeth fatigue in support vector machine,it provides theoretical basisfor cut teeth fatigue analysis in support vector machine.Aiming at the characteristic of tool head suffer heat stress influence,to the tool head proceedthermal fatigue analysis.in order to improve forecast accuracy of support vector machine,thesimulated annealing algorithm is established the mathematical model,realize support vectormachine the optimization of the superparametric parameters. Establish cut teeth life predictionmodel under load conditions,the forecast result and ANSYS/FATIGUE are compared,verify thesupport vector machine (SVM) method for the accuracy of forecasting cut teeth life.