Design Of Intelligent Monitoring System For Spray Painting Shop Based On Digital Twin

To solve the rotary parts spray paint process,difficult and low degree of automation,inadequate real-time monitoring,the problem such as poor working conditions,this paper designed a based on the number of twin paint shop intelligent monitoring system,it can be based on digital twin technology realize real-time monitor the working flow of spray paint,and based on the genetic algorithm(GA)to optimize the BP neural networks for fault diagnosis of the paint shop,Based on knowledge reasoning,fault remote operation is realized to achieve the effect of " The man-machine isolation ".Finally,a software platform is developed to integrate the above functions.The main contents and conclusions of the study are as follows:(1)Construction of monitoring model based on twinning.Firstly,the work environment,process and fault type of the paint shop are analyzed to provide support for the building of the paint shop model.Second workshop sensor network for the layout and the related sensor scene perception of multi-source information through communication network to the remote control for rapid extraction,sorting,complete the unified expression of data interface,and based on the rules of least squares and maximum likelihood registration algorithm is the basic information of the paint shop registration,so as to realize the primary of paint shop site data fusion.Then,3D modeling technology is used to model the equipment and environment of the spray shop,and the virtual working environment model is established.The real-time information of the scene and the 3D virtual model are interacted with multi-source information.Finally,the twin model of the automatic spray shop is obtained,and the intelligent monitoring of the spray shop is realized.(2)Design of workshop abnormal monitoring event response method.Firstly,GA-BP neural network algorithm is used to establish the fault diagnosis model of paint shop optimized by genetic algorithm BP neural network,and on this basis,the simulation test proves that GA-BP neural network has improved the accuracy of fault diagnosis compared with only using BP neural network.Through GA-BP neural network,the fault diagnosis of paint shop is realized.Secondly,the relevant knowledge base and support library are established to store the historical data and troubleshooting methods of the paint shop operation.Finally,the fault handling method of the painting process is obtained by querying the knowledge base,so that the corresponding fault can be handled remotely through "remote operation".(3)Build and test integrated software system.In order to manage the intelligent monitoring system uniformly,the software integration platform is divided into three modules,including normal monitoring module,teleoperation control module and information management module.The normal monitoring module can monitor the running status of the equipment in real time,and enhance the display of the equipment fault by sound,red,pop-up window and other ways to remind the control personnel to deal with;Remote operation control module can provide technical guidance for the staff by querying the relevant support database and knowledge base so as to remotely troubleshoot faults.In the information management module,you can find the operation data of the workshop and update the knowledge base.And,after the system test,the workshop of all kinds of fault diagnosis and teleoperation processing can be achieved,painting efficiency has been greatly improved.