Research On Group Preventive Maintenance Method Of Multi Parts Of Equipment Based On Digital Twin

Turbine machinery is the core equipment in the fields of metallurgy,petroleum,and chemical industry.Once an unplanned shutdown occurs,it will cause great economic losses to the enterprise.For large turbomachinery units,the existing maintenance methods generally estimate the performance interval of the equipment based on probability,and then maintain the individual components of the equipment based on the estimated results,but this method has not been carefully considered that are various correlations between equipment components,and there may be certain deviations that cannot meet the needs of intelligent maintenance.Therefore,it is necessary to propose an efficient and economical maintenance method.This thesis is based on the major scientific and technological project of Sichuan Province in 2019-"Advanced Manufacturing Intelligent Service",and taken large turbine machinery as the research object.It is based on the preventive maintenance theory for the problems of backward and inefficient maintenance methods of large machinery.And reliability theory,combined with group maintenance of components and digital twin technology,to carry out research on group preventive maintenance of multiple components of large equipment,mainly including the following parts:Firstly,for the problem of low historical data and failure data of turbine machinery,combined with the popular digital twin technology in the industry,a digital twin model is established for some components of the equipment,and the operating data of the twin model is trained according to the collected data to make it close to the real situation,the digital twin simulation data of the equipment operation is used for the following analysis,and the degradation process of the main components is analyzed according to the operation data to obtain a quantitative model of the degradation degree;Secondly,based on the operation data of the digital twin combined with the degradation process,the correlation between the components was studied,and an analysis method of the degradation correlation between the components was obtained.Based on that,the independent reliability of the components and the reliability of the equipment based on the correlation were further analyzed.And then,combined with the economic relevance and structural relevance of the components in the maintenance process,the cost model of the components in the maintenance process is established to provide a basis for the later study of preventive maintenance methods;Finally,based on the degradation process,reliability,and maintenance cost established above,a multi-objective preventive maintenance problem model is established.Combined with the collaborative algorithm,the search and update formula of the particle swarm optimization algorithm is improved to solve the model,and the individual components are obtained.Maintain windows,etc.,and compare and verify the effectiveness.At the same time,based on the window obtained by the above solution,a clustering algorithm with improved cluster radius is used.Combined with examples,preventive maintenance using the group method and traditional probability-based single.The preventive maintenance of components is compared through the simulation operation of digital twins,which shows the applicability of the method proposed in this paper in the actual situation,which can save maintenance costs to a certain extent,improve the available time after maintenance,and maintain the turbine machinery.It has certain guiding significance and practical value,and has certain enlightening significance for other similar large-scale mechanical equipment.