Study On The Optimization Of Equipment Health Evaluation And Maintenance Methods

With the development of modern industry,equipment presents a trend of refinement and complexity.The healthy and reliable operation of equipment is the basis for good operation of enterprises,and also affects the economic and social benefits of enterprises.In the actual investigation of state-owned enterprises,it is found that the difficulty of equipment maintenance is increasing.Therefore,it is important to accurately judge the health status of equipment for equipment maintenance.In particular,traditional equipment maintenance methods are difficult to meet the requirements of enterprise development.For example,the equipment information code lacks uniformity,and the enterprise’s internal physical code and equipment asset code are coded separately.The equipment maintenance personnel mostly maintain the equipment after the event.They do not have enough control over the health status of the equipment and cannot take maintenance measures before the equipment failure occurs.The priority of equipment maintenance is not scientific,and it is difficult to ensure that the equipment with the worst deterioration of health status is maintained first.This thesis takes the equipment management of state-owned assets government investment projects that S Company in T City is responsible for developing and operating as the research object.Firstly,the current situation of equipment management in state-owned enterprises is described,and the theories of equipment health management and predictive maintenance methods are discussed through literature review.Secondly,the factors that affect the health status of equipment are classified by using the literature analysis method and combining with the actual management characteristics of S Company.The AHP and fuzzy comprehensive evaluation method are combined to evaluate the causes and extent of the impact on the health status of S Company’s equipment.It is concluded that the factors that have the greatest impact on the health status of equipment are equipment coding and equipment maintenance methods.In order to solve the problem of equipment coding,a coding system based on GB/T 14885-2010 is built to create a unique code for equipment life-cycle management,laying the foundation for digital management of equipment.In order to optimize traditional equipment maintenance methods,a predictive maintenance method for equipment is proposed.Support vector machine(SVM)algorithm is selected to train the data set and create a visual page of prediction results.Equipment maintenance personnel decide maintenance measures and order according to the prediction results,so as to maintain the most seriously deteriorated equipment in the shortest time.Finally,optimize the platform construction,supervision system,personnel quality and other aspects to improve the equipment management level of S Company.Through the example verification,the research on the optimization of equipment health status evaluation and maintenance methods proposed in this paper has certain feasibility.Through the combination of qualitative and quantitative methods,the influencing factors of equipment health status are accurately identified and evaluated,and the artificial intelligence method is used to provide decision-making basis and scientific support for equipment maintenance.The unnecessary maintenance activities and maintenance costs of the equipment are reduced.It has improved the reliability and maintenance level of equipment,and helped enterprises transform and upgrade from traditional operation and maintenance methods to a new digital and intelligent mode.