Research And Application Of Maintenance Decision For Large Optoelectronic Equipment Based On Availability And Failure Prediction

As an important instrument for astronomical observation and tracking,optoelectronic equipment is developing in the direction of large-scale and complex,rich information interaction between many components,leading to increased equipment failure modes,need to regularly maintain some key components.However,in practical applications,people usually make decisions about maintenance cycles and maintenance behaviors based on experience,which can easily lead to excessive or insufficient maintenance frequency,which in turn increases equipment maintenance time and maintenance costs.In addition,the working environment of the photoelectric equipment is bad,and the distance between the on-site maintenance personnel and the experts who communicate the maintenance plan is far,resulting in the untimely implementation of the maintenance plan.In order to improve the availability of optoelectronic equipment,reduce maintenance costs,and improve maintenance efficiency,there is an urgent need to implement more targeted and professional maintenance decision-making methods.This article studies the maintenance strategy of optoelectronic equipment around the maintenance cycle and maintenance behavior,and establishes a networked maintenance decision-making platform,specifically completing the following tasks:Adopted the method of equipment maintenance cycle decision based on availability.Through the life distribution characteristics of each component of the equipment,and consider the impact of working hours on equipment availability,thereby establish the relationship model between the availability of multi-component equipment and the timing of maintenance.By studying the functional relationship between equipment availability and maintenance cycle,with availability as the optimization goal,the best maintenance cycle of equipment was determined.Adopted maintenance behavior decision method based on failure rate prediction.Aiming at the problem of small data samples of research objects,the combination of grey theory and linear regression is used to predict the failure rate,which improves the prediction accuracy.In order to reduce the total cost of maintenance including failure risk cost,AHP was introduced to evaluate the failure risk cost.Using the failure rate evolution method,obtained the failure rate after maintenance.Comprehensively consider the two factors of the reduction rate of the failure rate before and after maintenance and the maintenance cost,and use the cost-effectiveness ratio to decide the maintenance behavior.The thesis takes a large-scale optoelectronic device as the research object,applies the above method to practice,and obtains the decision results of the device maintenance cycle and maintenance behavior.According to the actual needs of the project,a decision-making system for the maintenance of optoelectronic equipment based on B /S was established,Completed four major functional modules: operation monitoring,monitoring management,task selection,task list,new task creation,and maintenance decision report,the paper describes the system structure and functional modules.