Risk Analysis Of Machining Center Based On Improved FMEA

As a complex electromechanical equipment integration system,machining center is widely used because of its strong comprehensive machining performance.However,the strong structural and functional dependence between components leads to high coupling of failure modes,which improves the probability of failure of each subsystem and the whole system and reduces its reliability level.Therefore,considering the failure related failure risk analysis of machining center is of great significance to accurately identify the key failure modes and subsystems and improve the reliability level of product design.Firstly,the improved regression conversion recursive algorithm is used to fuse the failure data of a machining center and its products for the first time.The failure data of similar products are mathematically modeled,the model parameters are solved by maximum likelihood estimation method,and the parameter point estimation is transformed into interval estimation by Fisher information matrix.Under the condition that the failure model and the product model are similar to each other,the Monte Carlo information fusion model is established,and the product reliability is obtained by using the Monte Carlo information fusion after the first sample and the second sample are regarded as the a priori estimation of the failure model.Secondly,combined with the reliability model of failure mode,C Vine-copula and R Vine-copula are established respectively.The optimal model is selected through AIC criterion and BIC criterion,the pair copula type and its corresponding parameters in each layer of tree structure are determined,and the dependency structure between the occurrence probabilities of failure mode is described.By calculating the upper tail correlation coefficient,lower tail correlation coefficient,Kendall rank correlation coefficient and copula density function images,the tail correlation and overall correlation of the occurrence probability of each failure mode of the machining center are analyzed.Finally,the interval two-dimensional semantic language variable is introduced to represent the risk factor,which is transformed into the form of interval number by using the transformation function.The expert weight is solved by the expert weight iterative algorithm,and the risk factor weight is solved by the interval number entropy method.The improved interval number risk priority number of each failure mode is obtained,and the ranking result of failure modes is obtained by TOPSIS method.The failure mode is mapped to each subsystem of the machining center through the directed causality diagram,and the transmission probability of the failure mode is solved by using the dependency relationship between the failure modes described by the copula function.Combined with the improved interval number risk priority number of the failure mode,the failure cause effect correlation coefficient in the system and the failure cause effect correlation coefficient between the systems are calculated respectively,and the sum of the two is taken as the system importance index.Combined with TOPSIS method,the importance ranking of machining center subsystem is analyzed and obtained.