Gradual Reliability Analysis Of Driving Parts Of Shearer Cutting Part

Although the domestic shearer can basically meet the production needs of domestic coal enterprises,there is still a big gap compared with the world’s advanced shearer,which is mainly reflected in the aspects of shearer intelligence,failure rate,overall performance,service life of parts,etc.How to reduce the gap with foreign shearers has become the primary problem,and one of the important research contents is to improve the reliability of the key parts of the shearer.Aiming at the reliability of the key parts of the shearer,taking the transmission parts of the cutting part of the MG750/1815-WD-type shearer as the object,considering the strength degradation of the parts and the randomness of the external load under the working condition,the gradual reliability of the transmission parts is analyzed,the influence of the correlation on the reliability is given,and the gradual reliability robust optimization design for the structure of the most easily damaged parts is carried out,The specific contents of this paper are as follows:(1)In order to solve the problem of gradual change reliability of torque shaft caused by strength degradation and random load,Goodman’s line correction method and linear accumulation damage method are used to estimate the fatigue life of torque shaft.The mathematical model of gradual change reliability is established by using the method of maximum load replacing load change in equal time period and gamma process simulating strength random regression.At the same time,the gamma process parameters are analyzed by the P-S-N curve of material,then the reliability and sensitivity curves of the torque shaft in the fatigue life are obtained by using gamma process parameters.The influence degree of each parameter variable change on the gradual reliability of the torque shaft is analyzed and verified by the Monte Carlo simulation results.(2)The parametric modeling of the idler shaft is carried out,the database of the random maximum stress and parameter variables of the idler shaft is obtained by using the Workbench experimental design,the functional relationship between the maximum stress and the random parameter variables is fitted by using the neural network,the fatigue damage of the idler shaft in a load history is analyzed by using Ncode DesignLife,the limit state equation of random maximum stress and strength degradation is constructed and the gradient reliability analysis is carried out.(3)Considering the influence of correlation on the gradual reliability of transmission parts,this paper first discusses the random parameter correlation of torque shaft and idler shaft,and analyzes the influence of the size correlation of torque shaft and idler shaft on the gradual reliability.Then,a Copula function theory is given to analyze the correlation of various failure modes.On the basis of a single failure mode,the correlation between various failures is considered comprehensively,and the reliability problems of gears and splines of gear shaft under common multiple failure modes are studied.(4)In this paper,the gradual reliability robust design optimization method is given.Aiming at the problem of torque shaft robust optimization design,three schemes are put forward,which include taking the mean sensitivity of reliability as the objective function,the quality as the objective function,the mean sensitivity and the quality as the objective function,taking the mass as the objective function,taking the mean sensitivity and the mass as the objective function under the given reliability constraint.Aiming at the problem of inert shaft robust optimization design,the reliability and the mass under the given reliability constraint are put forward Reliability sensitivity and mass are two schemes of objective function.The curve of their size design parameters with the optimization time is obtained by optimization.According to the curve,the optimal size at any time can be found quickly and accurately.Finally,the sensitivity of the size parameters before and after optimization is compared to verify the correctness of the optimal size.