Integrated Optimization Of Predictive Maintenance And Production Scheduling Considering Machine State

The maturity of the Internet of Things technology has promoted the development of industrial modernization and accelerated the process of intelligent transformation of the manufacturing industry.How to realize the digitalization of workshop is the key to the transformation of manufacturing enterprises.However,due to the "black box" problem caused by the difficulty of human-computer information interaction,the lack of timely and comprehensive information interaction,and the low degree of visualization in most enterprises,the management personnel’s insufficient monitoring of the implementation information at the bottom of the workshop seriously restricts the development of workshop intelligence.In this paper,the integrated optimization of production scheduling and equipment maintenance is studied to solve the contradiction between them,find the best balance point between them,and establish a production scheduling model that is in line with the actual situation,which has important guiding significance for the production scheduling problem of the enterprise workshop.The model of machine state prediction and analysis is established.The machine state in the future period is predicted and the failure time node is obtained by considering the continuous change of machine state.According to the state data,the spectrum clustering algorithm is used to divided the whole life cycle of the machine,so that the state of the machine can be quantified.According to the clustering results and the machine state prediction curve,the fault points are analyzed,the fault data are obtained,and the fault distribution function of the machine is calculated.This paper proposes a new maintenance strategy,which took the state interval of the machine when it breaks down as the decision-making basis of maintenance activities.Considering the two maintenance methods of perfect maintenance and imperfect maintenance of the machine,a model with the maximum processing time and the minimum as the optimization objective is established.The improved bat algorithm is used to solve the problem,and the model is verified by an example.The results show that the established model has certain effectiveness and applicability,and has certain guiding significance for enterprise production.A multi-objective integrated optimization model and a genetic algorithm(NSGA-Ⅱ)for job shop considering predictive maintenance are established.The factors of product delivery time and cost are introduced.The machine failure rate is included in the production scheduling of predictive maintenance shop,and the change of machine failure rate is considered.According to the current processing time of the parts to be processed,the state of the machine is predicted,so as to realized the decision-making of the machine activities and maked a reasonable and effective production plan.A prototype system of shop floor management is developed,which can predicted the failure of the machine and allocate resources to the considering predictive maintenance.Effectively solved the contradiction between production scheduling and maintenance activities,improved the efficiency of the machine.