Optimization Of Preventive Maintenance Policies For Aging Equipments

With the improvement of production technology, manufacturing enterprises continue to expand the productive capacity. The productive equipments are large, high-speed, intelligent, continuous, automated and complicated. To ensure the reliability, stability and economy of the equipments, the project of equipment maintenance management has become an important field of study.In this paper, based on the maintenance data of the equipments in an automobile factory engine block production line, according to the times of equipment failures, single equipment mean time between failure, maintenance time and equipment maintenance costs find the key equipment, using the method of Improve Factor and the concept of Incomplete Maintenance, assuming that the equipment failure rate subject to the Weibull distribution, building an optimization preventive maintenance model which have the minimum target function during limited period of time for aging equipments. The model set a limited time interval, the relative total cost of preventive maintenance is divided into three parts which are the relative minimal maintenance costs of preventive maintenance, the relative cost of preventive maintenance and the relative cost of production expense cause by preventive maintenance, the validity of the model was verified by using experience parameters, calculate the times of maintenance and the best maintenance time interval during the limited time, measuring the reliability after each preventive maintenance for the equipment, and analyzing the sensitivity of adjustable parameters. Finally, compared with the optimization model before, the optimization model can solve the complex problems of preventive maintenance policies through a simple preventive maintenance model to improve the availability and effectiveness of preventive maintenance policies for aging equipments. It's very useful for developing the maintenance plans and the job scheduling in practice.This first chapter briefly described the research background of the equipment maintenance policies, the purpose and the scope of study, which building an optimization preventive maintenance model which have the minimum target function during limited period of time for aging equipments. Finally, the organizational chart of study is given.The second chapter described the relevant literature of preventive maintenance policies. From the definition of maintenance, the three stages of equipment maintenance management development are outlined. Based on the five different maintenance policies, various incomplete maintenance models are detailedly described. Finally, the different optimization methods of incomplete preventive maintenance models are introduced.The third chapter using the method of Improve Factor and the concept of Incomplete Maintenance, assuming that the equipment failure rate subject to the Weibull distribution, building an optimization preventive maintenance model which have the minimum target function during limited period of time for aging equipments.The fourth chapter based on the maintenance data of the equipments in an automobile factory engine block production line, according to the times of equipment failures, single equipment mean time between failure, maintenance time and equipment maintenance costs find the key equipment, calculate the times of maintenance and the best maintenance time interval during the limited time, measuring the reliability after each preventive maintenance for the equipment, and analyzing the sensitivity of adjustable parameters.The fifth chapter compared with the optimization model before, that optimization model can solve the complex problems of preventive maintenance policies through a simple preventive maintenance model to improve the availability and effectiveness of preventive maintenance policies for aging equipments. It's very useful for developing the maintenance plans and the job scheduling in practice.The last chapter of this paper gives the conclusions and future research directions of this study.