A Method For Analyzing Abnormality Of Automobile Sunroof Manufacturing Process By Using Bayesian Method

Automobile skylight is a more complex exterior decoration system,which consists of frame group,mechanical group,motor group,glass group and sunshade.Nowadays,the car skylight has gradually become the standard configuration of the car,and the beauty and functionality of the car's exterior decoration have gradually been valued by people.However,more than half of the abnormalities in the car's shell are caused by the car skylight,especially the abnormal noise problem,which has always affected the driving experience of the car users.Therefore,when the car skylight is not disassembled,timely and accurately make the faults It is very important to judge the fault position,find out the cause of the fault and put forward the solution to reduce the blindness of the repair of the sunroof.In the process of vehicle sunroof fault diagnosis,due to the complexity of the diagnosis object,the limitation of the test method and the inaccuracy of the knowledge expression,especially for the complex diagnosis object of vehicle sunroof,there may be many complex forms of multi-source faults and related faults between its interior and components,but the causal relationship between the fault phenomenon and the fault cause is not sure,but it Because of the strong uncertainty and randomness,it is difficult to determine the real cause of the fault.Therefore,the traditional fault diagnosis technology is more and more difficult to meet the reliability requirements of people for complex equipment diagnosis,which requires us to explore a diagnosis method with strong analysis ability and solving uncertainty problems.Bayesian network based on Bayesian method has many advantages in analyzing and solving uncertainty factors.The quality control in the production process of automobile skylight is of great significance to improve the yield and reduce the cost.In the automobile skylight manufacturing industry,due to the human,machine,material,manufacturing method,measurement method,environmental factors and other factors,the final product quality will be affected.So the application of traditional expert system diagnosis and analysis method is limited.In view of this situation,this paper uses Bayesian network to establish a diagnosis method for the quality abnormality in the production of automobile sunroof,using the prior knowledge in the manufacturing process of sunroof,combined with the abnormal symptoms that need to be diagnosed,to provide ideas for the abnormal diagnosis in the production process of automobile sunroof.According to the working principle and fault symptoms of the vehicle skylight,the Bayesian network model of the fault information of the vehicle skylight system is established by using the simple Bayesian topological structure.At the same time,based on the particle swarm multiple optimization algorithm and Bayesian algorithm,the Bayesian network structure and parameter learning method are studied,and the online sample of the vehicle skylight network diagnosis model is realized by using this method Learning,and then through the application of case analysis to verify that the online Bayesian network fault diagnosis model has a higher accuracy than the traditional Bayesian network model,and on this basis,using MATLAB,VB and other development tools to complete the design of the corresponding module of the vehicle sunroof fault diagnosis system,and finally the system will be applied to the vehicle sunroof fault diagnosis.