Research And Implementation Of Key Technologies For Health Status Evaluation Of High Speed Train Automated Assembly Line

With the rapid development of high-speed railway technology,the railway line has been continuously expanded,the assembly efficiency of high-speed rail train bogies has become increasingly prominent.Moreover,along with the advent of the industrial 4.0 era,the intelligent industrial assembly line is gradually emerging,and the degree of automation of enterprise workshops is getting higher and higher.How to ensure the steady and efficient operation of an automated assembly line has become a major issue that enterprises are increasingly paying attention to.Faced with such a large automated assembly line,once a key component fails,it will affect the operation of the entire assembly line and bring serious economic losses.Therefore,abandoning the traditional time-consuming maintenance strategy such as fixed maintenance and after-repair,adopt a maintenance strategy based on the health status of equipment,not only to ensure efficient assembly line operation,but also can greatly save manpower and resources,improve economic efficiency.The core technology of state repair lies in using the actual online monitoring data to conduct an effective health assessment of the equipment,which is also an important part of PHM(Prognostics and Health Management)technology.On the basis of the study of the classic PHM algorithm and the real-time data characteristics of the motor drive system,in this paper,the fuzzy clustering algorithm(FCM)in unsupervised learning was used to study the health status assessment.Aiming at the defects of the traditional FCM algorithm,the corresponding optimization is made and an effective evaluation model is established.Using probability of membership form,all data are divided into three categories:normal,warning,serious.This paper has realized the evaluation of the health state of the motor drive system,and completed the important combination of the machine learning algorithm and the industrial production.The specific research has the following points:(1)Due to the random selection of the initial value of traditional FCM algorithm,which can lead to local minimum,in this paper,the genetic algorithm is used to optimize FCM.Use the FCM criterion function to create a fitness function,and construct adaptive crossover and mutation operator.So that the optimal initial cluster center can be dynamically selected to avoid falling into local minimum.Through common data set validation,GA-FCM algorithm can reduce the initial class center of the objective function value,thus reducing the number of convergence,to ensure the stability of clustering and avoid falling into a local minimum.(2)There are a lot of outliers and noise points in the actual engineering data,which severely affect the clustering of FCM.This paper uses a method based on distance weighting to rewrite FCM's criterion function,make the final clustering centers locate in the area where the density is high.This paper combines FCM algorithm,genetic algorithm and weighting strategy,called WG-FCM algorithm,which can not only select the optimal initial center,but also effectively avoid the impact of noise points on cluster classification.(3)A health state assessment model based on WG-FCM algorithm is constructed.In this paper,the actual motor drive system data is used to verify the results,it show that WG-FCM algorithm can get better clustering partition than GA-FCM,and the clustering results are more reasonable.The research results are integrated into the actual monitoring platform to complete the motor drive system health assessment.