Analysis And Research On Reliability Model Theory Of Numerical Control Machine

Since the numerical control machine reliability model theory is the foundation of reliability analysis,the two-parameter and three-parameter Weibull Distribution are the most two common failure distribution models in current time failure model research;however,the two distribution models are not able to describe collection interval of failure data sufficiently so doubly-truncated Weibull distribution model is introduced.For conventional reliability analysis process,make regression analysis or curve fitting and then make hypothesis testing for machine failure data;finally complete machine assessment.Meanwhile the defect of model parameter interval assessment method affects completeness of CNC reliability analysis.To distinguish analysis model of machine failure data,make regression analysis or curve fitting for data by taking advantage of various models and judge which distribution model the failure data is subject to through hypothesis testing.With development of reliability work and improvement of manufacture technology level,it is more and more difficult to collect failure data and the costs are raised gradually.The special testing method of two-parameter Weibull distribution model,the most common failure model in machine reliability analysis can improve analysis efficiency largely.The appearance of k-mean clustering algorithm based RBF neural network reliability extension algorithm can improve assessment difficulty caused by small sample data largely but K value is hardly determined in the algorithm.At present,there is not a common method not depending on experts’ experience relatively.With various model of numerical control machine failure data collected by many enterprises through cooperation,the various distribution models of parameter estimation methods and hypothesis testing methods are researched in this paper;the parametric interval estimation for three-parameter and doubly-truncated Weibull distribution model are explored;the new failure data extension algorithm is proposed.The main research achievements are set forth below:In theoretical perspective,the various distribution models of parametric point estimation and interval estimation method are analyzed deeply and the parametric point estimation and interval estimation methods of failure models are researched based on several groups of experimental data.In numerical control machine electrical appliance failure data analysis,the Weibull distribution model is more efficient than index distribution and gets smaller confidence interval at the same confidence level.In complete machine failure analysis,two-parameter Weibull distribution model also performs well.The special hypothesis testing method for two-parameter Weibull distribution,Mann test method is proved theoretically and verified efficiently based on several groups of experimental data;since Mann testing is quite accurate in small sample analysis,the new numerical control machine reliability analysis process is proposed and Mann testing method should be used for testing preferentially if the two-parameter Weibull distribution model data are simulated.The interval variation iterative algorithm is designed to solve parametric point estimation for three-parameter Weibull distribution model and the parameter interval is estimated and analyzed on the basis of likelihood ratio testing theory.By analyzing impact trend of position parameter for confidence interval enclosed by dimension parameter and shape parameter,it can be concluded that the position parameter of three-parameter Weibull distribution model does not have confidence interval smaller than scope of value.The parametric point estimation method for doubly-truncated Weibull distribution model on the basis of maximum likelihood estimation and extreme value thought is proposed with likelihood ratio testing theory-based parametric interval estimation method.The best initial interval determination algorithm should be designed and interval variation iterative algorithm is used for solution based on experimental data and Monte Carlo method.The AP clustering algorithm based RBF neural network extension algorithm is put forward;the total sample related reference is designed;the dependence on experts’ experience is reduced.BWP clustering optimization index is introduced and the testing process is designed.The various sample sizes of two-parameter Weibull distribution analog data and one group of machine testing failure data are used for contrastive analysis.