Research And Implementation Of Key Technologies For Aviation Equipment Support Maintenance Forecast

With the development and progress of science and technology,the aerospace equipment technology innovation and conversion frequency are also increasing rapidly.The new era battlefield requires the aviation army to have fast execution capabilities and strong strike capabilities.This requires further upgrades of aviation equipment support and maintenance to meet the requirements.In aviation equipment maintenance and support,it is essential to reasonably arrange aviation equipment maintenance resources,predict the life of aviation equipment with life parts,and manage related aviation equipment information.It is a very complicated problem to predict the replacement of life parts of aviation equipment.The life of aeronautical equipment with life parts is affected by many factors,and the prediction of damage replacement of life parts has great randomness.At the same time,the prediction of aviation equipment spare parts also has many factors.These make the prediction of damage and replacement of aviation equipment inaccurate and increase the difficulty of related staff and task amount.Pointing at these problems,this paper proposes a genetic algorithm and neural network method to solve the damage replacement prediction of aviation equipment with life parts and the prediction of the number of aviation equipment spare parts.It solves the problem that the related personnel are cumbersome and not timely handling the daily aviation equipment maintenance and support.It can accurately predict the use situation of equipment with life parts,and replace it before an accident,which will not only reduce the occurrence of accidents,ensure the daily training and completion of tasks of the army,but also greatly reduce the related staff's workload.First process the data,use the genetic algorithm to select the processed data,cross and mutate operations to get the initialized weights and thresholds,and then usethe neural network to use the life time,number of times,friction coefficient and temperature of the life parts equipped factors predict the damage of aviation equipment with life parts.Predict the number of aviation equipment spare parts through relevant data.Finally,it is verified through simulation experiments that the genetic neural network is used to make the prediction of damage replacement of aviation parts more accurate.For the design and prediction of aviation equipment,the aircraft are simulated.In order to better optimize and predict the damage of aviation equipment,this paper proposes a digital twin technology that can optimize the operation of simulation,mitigate risks and increase efficiency.Digital twin can more accurately predict the life of aviation equipment and detect abnormalities.Aiming at the problems of heavy information management,complicated work procedures,and regulations of the aviation equipment maintenance and support system.this article designs and develops an aviation equipment support and maintenance prediction system to ensure that staff can work in time and understand aviation equipment maintenance support information,and timely predict the components to be repaired or replaced by aviation equipment,in order to speed up the maintenance speed,shorten the maintenance cycle,make the maintenance support work more efficiently,and the aviation equipment is more reasonable and more fully utilized.The timely replacement of the aviation equipment's life parts guarantees the reliability of the equipment,thereby meeting the combat capability and response capability of the army.