Optimization Decision Method For Equipment Maintenance Support Based On Particle Swarm Optimization Algorithm

With the development of China’s civil aviation industry,the requirements for aircraft maintenance by civil aircraft operators are gradually increasing.The initial preventive maintenance plan provided by civil aircraft manufacturers can no longer meet the maintenance needs of civil aircraft with the characteristics of automation,multi-function and structural complexity used by airlines.High maintenance costs will directly lead to the increase of operating costs of airlines,and low aircraft utilization will directly affect the economic benefits of aircraft.The initial preventive maintenance plan provided by aircraft manufacturers such as Boeing and Airbus is one of the important factors affecting their share in the global civil aircraft market.This article conducts in-depth research on the optimization of preventive maintenance intervals for civil aircraft components,and establishes a corresponding maintenance interval optimization model.The particle swarm optimization algorithm is improved to solve the optimal maintenance interval.Firstly,with the maximum availability of civil aircraft components as the optimization objective and the reliability threshold as the constraint condition,a single objective maintenance interval optimization model was constructed to obtain the optimal preventive maintenance task strategy for components.In order to effectively solve the optimization problem of single objective preventive maintenance intervals for civil aircraft equipment,this paper proposes an optimization algorithm based on the Contract factor Particle Swarm Optimization Gravity Search Algorithm(CPSOGSA).This method utilizes the group search ability of GSA algorithm to solve the defects of particle swarm algorithm such as "premature" and easy to fall into local optima.At the same time,the shrinkage factor is used to adjust particle swarm group updates,improving the global optimization performance of PSO algorithm and effectively improving the accuracy of single objective maintenance interval optimization.Through numerical examples,it has been proven that this method can be effectively applied to optimize the maintenance interval of civil aircraft equipment,and the estimation results have high accuracy,indicating the effectiveness and feasibility of this method.Maintenance cost is also a key concern for airlines.Based on this,maintenance cost rate is introduced and a multi-objective optimization model is established with reliability threshold as constraint conditions,while meeting the minimum cost rate and maximum availability.In order to improve the performance of multi-objective particle swarm optimization algorithm and better solve multi-objective maintenance interval optimization problems,This article proposes an adaptive multi-objective particle swarm optimization algorithm based on congestion distance and elite selection strategy(MOAPSO-CD-ELS).This method utilizes parameter adaptive control strategy to ensure the global optimization ability of particles,ensures the diversity of solutions through crowding distance,enhances the ability of particles to jump out of local optima through elite selection strategy,and verifies the comprehensive performance of the solution set obtained by this method,such as uniformity,particle diversity,and convergence speed,through testing functions,The calculation of an example proves that this method can be well applied to multi-objective maintenance interval optimization of civil aircraft equipment,and the optimization results have high accuracy,indicating the effectiveness and feasibility of this method.Through the collected historical failure data of a certain type of civil aircraft equipment components,the feasibility of the single objective and multi-objective optimization models proposed in this paper is verified,and the results are compared.At the same time,according to the actual situation,a preventive maintenance plan for civil aircraft equipment components that meet the corresponding objectives is formulated.Through the example verification,it is proved that the improved particle swarm optimization can be well used for the maintenance interval optimization problem,providing theoretical support for civil aircraft manufacturers and airlines to develop maintenance plans,so as to better help airlines control operating costs,and provide strong support for civil aircraft manufacturers to occupy a favorable market share.