| With the economic globalization and the rapid development of science and technology,the production scale of enterprises is expanding,the structure of the equipment is more and more complex,the function of the equipment is more and more perfect,the degree of automation is more and more high.In the operation of the equipment,the equipment will deteriorate due to fatigue,wear,aging and various uncertainties in the dynamic environment.The efficiency and performance of the equipment will decrease gradually.If the equipment is not repaired,it will eventually fail or even shut down,resulting in equipment failure rate and production costs increases,as well as product quality reduction.Therefore,in order to reduce such huge losses,it is of great significance to establish the monitoring equipment status and fault analysis management mechanism,and to carry out preventive maintenance,and prevention.In this paper,we study the packaging equipment of medicinal--aluminum-plastic blister packaging machine,analyze the composition and process flow of blister packaging machine,to determine the status of equipment.Then,DMAIC tools are used to define the need for equipment maintenance,to measure and analyze equipment failure data,to eliminate non-critical faults and to identify major downtime failures.After the main faults are identified,the preventive maintenance of the main faults is carried out by using the maintenance strategy formulated by the reinforcement learning method so as to achieve the purpose of reducing the equipment failure rate,decreasing the production cost and improving the packing efficiency of the equipment.After the final comparison of data,equipment packaging efficiency has been significantly improved,indicating that the above method is effective in practical applications.Therefore,the research work in this paper has some reference significance for the equipment fault analysis and maintenance,and can provide good economic benefits for the related enterprises. |
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