Research On Fault Prediction And Maintenance Strategy Of Intelligent Equipment In Enterprise A Under Digital Twin Framework

Under the "dual role" of the strong demand for transformation and upgrading of the manufacturing industry and the rapid development of a new generation of information technology,the precision,complexity and intelligence of the equipment used in the industrial production process are getting higher and higher.While these smart devices improve productivity,they also place higher requirements on enterprise device management.Equipment management,as an important part of production management,directly affects the production plan,economic benefits and safety benefits of enterprises.If the equipment is poorly managed and machine failures occur frequently,it will not only disrupt the normal production plan of the enterprise,increase the production cost of the enterprise,but also may cause human casualties.Therefore,how to use scientific and reasonable equipment failure prediction methods for smart devices,master the degradation rules of smart devices and formulate corresponding preventive maintenance strategies have become an urgent problem for manufacturing companies.In order to accurately predict failures and formulate preventive maintenance strategies,this paper takes the pyroelectric automatic test machine of A company as the research object.Firstly,in order to ensure the timeliness of fault prediction,the framework of fault prediction and maintenance of intelligent devices is constructed by using digital twin technology,and real-time operation data of intelligent devices are obtained by using sensor technology,so as to provide data support for data-driven model;Secondly,the fault prediction and maintenance framework is used to collect real-time fault data and establish the initial fault decision table.BP neural network is used to carry out fault failure.According to the simulation results of BP neural network fault prediction,principal component analysis is used to optimize the input data of BP neural network.The fault prediction model of pyroelectric automatic testing equipment is established and the prediction results of several methods are compared and analyzed;Then,the fault prediction results are converted into the remaining service life of the equipment,and the remaining service life is an intermediate variable to construct a multi-objective dynamic optimal preventive maintenance strategy that satisfies the maximum availability of the pyroelectric automatic test machine and the minimum average maintenance cost rate.Model,obtain the node of the remaining life prediction and the remaining life threshold for preventive maintenance;Finally,the developed preventive maintenance strategy is fed back to the equipment itself,so as to realize the intellectualization of equipment maintenance in A enterprise.The research results show that: The attribute reduction effect of principal component analysis is better than rough set and singular value decomposition,which can optimize the input of BP neural network and improve the accuracy of fault prediction,Which is conducive to mastering the degradation laws of smart devices;The preventive maintenance cycle established by comprehensive availability and average maintenance cost rate can effectively reduce the downtime and total maintenance cost of the pyroelectric automatic test machine;The fault prediction and maintenance framework can not only ensure the timeliness of fault prediction,but also realize the intellectualization of intelligent equipment maintenance.