Research On Workshop Operation Maintenance And Decision Method Based On Manufacturing Big Data

With the deep development of the integration of the two industries,manufacturing enterprises have stepped into the new industrialization road.For the discrete manufacturing industry in the multi-variety and small-batch mode,the manufacturing parts are complex and diverse,the production disturbance is frequent,and the production mode is flexible.In the context of the new generation of information technology,this research is based on the workshop production and operation ideas of “acquisition correlation + prediction identification + regulation decision-making”,starting from the big data of the part manufacturing process,based on workshop intelligent agent,the data of manufacturing process can be collected and processed reliably,using knowledge automation method and equipment predictive maintenance model to realize the operation and maintenance of discrete manufacturing workshop from the perspective of shop process management and equipment predictive maintenance,and verify the effectiveness of the above method through case analysis.The specific research content of this paper is as follows:(1)The theoretical framework of discrete shop operation and decision-making based on manufacturing big data is introduced.The current research and application status and development trend are expounded from three aspects: big data engineering,knowledge engineering and equipment predictive maintenance.(2)This paper analyzes the manufacturing characteristics of the discrete manufacturing workshop under the background of digitalized and intelligent manufacturing process,summarizes the existing problems and demands of the operation and maintenance of the discrete manufacturing workshop,optimizes and reconstructs the production management business process of the workshop,and designs the overall framework of the operation and maintenance of the discrete intelligent workshop based on the manufacturing big data.(3)A discrete manufacturing big data acquisition and processing platform based on the intelligent agent in the workshop has been built.Based on the data acquisition model,real-time description and acquisition of manufacturing process data are realized by using the intelligent agent in the workshop as the carrier.Clustering based discrete point detection to obtain equipment degradation adjustment parameters,for equipment predictive maintenance planning to provide a basis;Based on the process description model of process manufacturing,the whole process description information of process manufacturing can be integrated and encapsulated,which lays a foundation for the automation of process knowledge.(4)The knowledge automation method for process production is designed.Based on the process characteristics and FCM method,a strict knowledge base division logic and standard is constructed.According to the structural characteristics of knowledge base at different levels,different methods of calculating the index weight of process characteristics are used to realize the automatic acquisition of cases based on similarity element theory.Based on case-based similarity reasoning,the process condition recognition and prediction is realized.(5)Design the equipment decay evolution and multi-objective predictive maintenance model under time-varying working conditions.Based on the case-based similarity reasoning method,the paper introduces the operating condition adjustment factor and combines the traditional equipment recession evolution model to design the equipment recession evolution model under the discrete production mode.Based on the failure rate function under dynamic working conditions,multi-objective dynamic maintenance planning modeling is realized.