Configuration Design Of Metro Bogie Products And Maintenance Service Oriented To Cost Optimization

In recent years,urban rail transit in China has achieved vigorous development.Maintenance is an important service to ensure the safe and efficient operation of subway vehicles,since the custom design of metro vehicles currently focuses on the satisfaction of product functions and performance,the product design and maintenance service design have not yet formed a good coordination and integration mechanism,resulting in higher maintenance costs.In the design phase,a large part of the life cycle cost can be determined,which brings more development opportunities to the original equipment manufacturers.After the original equipment manufacturers undertake the full life cycle maintenance service of metro,it will help reduce maintenance cost and create new profit point.Bogie is the key component of metro vehicle,which has an important influence on the power performance,driving safety and running quality of the subway vehicle.Therefore,metro bogie is regarded as the research object,and a design method for the configuration of subway car bogie products and maintenance services oriented to the full life cycle cost is proposed.The main research contents are as follows:(1)Based on the analysis of the basic function and structure of bogies,the current status of metor vehicle maintenance services and the design principles of product service systems,the defectss of the existing bogie configuration design are analyzed,and the necessity of subway bogie products and maintenance service configuration is determined.On the basis of analyzing the characteristics of bogie products and maintenance services,the technical route is proposed.(2)In order to identify the product modules that need to be focused and optimized in the design process,the importance of the modules is evaluated from both user needs and failure risks.In view of the complex causal relationship between user needs and the uncertainty of the evaluation information proposed by experts in the construction of demand weights and house of quality,a key product module identification method based on fuzzy DEMATELANP-QFD is proposed.First,this method identifies the causal influence relationship between user requirements and draws the network of relation map based on fuzzy DEMATEL,next the fuzzy ANP method is used to solve the relative weight of demand.Then,the HOQ between requirements and engineering characteristics and the HOQ of engineering characteristics and modules are constructed,and the importance of product modules is calculated based on the fuzzy QFD method,then combined with the product RPN value analysis results,the product modules that need to be focused are determined,which lays the foundation for the construction of the configuration model.(3)The method of subway bogie products and maintenance service configuration design is proposed.Based on the identified key product modules and corresponding maintenance services,a bogie product and maintenance service configuration model was constructed.On this basis,combined with the characteristics of the multi-component and multi-maintenance strategy of subway bogies,a cost model is constructed to quantify the functional relationiship between purchase cost and maintenance cost and the configuration scheme,then the genetic algorithm is used to solve the model to obtain the most cost-effective product and maintenance service configuration plan.(4)In order to realize the dynamic evaluation and optimization of design solutions,a method of dynamic evaluation and optimization of product design schemes based on Bayesian network is proposed.This method can determine the conditional probability through expert experience,construct a Bayesian network for design plan review,and identify key design defects through sensitivity analysis to realize the iterative optimization of the design plan which does not meet the design requirements.Since the evaluation information collected during the use of the product may be different from the result of expert experience judgment,this method supports dynamic updating of Bayesian network parameters based on the collected user evaluation information through maximum likelihood estimation.