{"title":"The Performance of Radar Heat Dissipation System under Particle Swarm Optimization Algorithm and Structural Design of Front-end Prototype","authors":"Zhen Wang, Jinwen Zhou","doi":"10.1145/3421766.3421810","DOIUrl":null,"url":null,"abstract":"To clarify the problems of aviation radar in heat dissipation, optimize the overall operational capability of radar equipment, and improve the safety of aviation radar equipment, under the premise of studying the structure of the radar heat dissipation system, by analyzing the operation of the radar heat dissipation system and the motor of the front-end prototype structure, the main reasons for heat dissipation faults are deeply analyzed. The method of statistical process control is utilized to predict the performance of the front-end motor and remind maintenance personnel to monitor the radar heat dissipation system in real-time. At the same time, by using the improved particle swarm optimization (PSO) algorithm model, the factors and kernel functions of the support vector machine (SVM) are optimized, and the regression accuracy of the SVM is improved. Furthermore, the motor failure prediction model is established, thereby ensuring the efficient and safe operating state of the radar system. The results show: (1) the failure of the radar motor is the major cause of heat dissipation faults; (2) compared to other algorithms, the efficiency of the PSO algorithm is improved by 30%, but the accuracy rate drops by 5%; (3) the applications of forewarning model for front-end prototype under statistical process control (SPC) can reduce the workload of maintenance personnel by 50%. The simulation results show that the combined method of SPC and SVM can predict the failure of the powering devices in radar heat dissipation systems. Besides, if the classification and regression models are combined, the difference between the predicted voltage and the true voltage will be smaller, and the accuracy will be higher. The above results provide a theoretical basis for the research of radar heat dissipation system and motor failure, which ensures the overall safety of the radar system and provides the necessary guarantee for the crew and the aviation command system.","PeriodicalId":360184,"journal":{"name":"Proceedings of the 2nd International Conference on Artificial Intelligence and Advanced Manufacture","volume":"18 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2nd International Conference on Artificial Intelligence and Advanced Manufacture","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3421766.3421810","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
To clarify the problems of aviation radar in heat dissipation, optimize the overall operational capability of radar equipment, and improve the safety of aviation radar equipment, under the premise of studying the structure of the radar heat dissipation system, by analyzing the operation of the radar heat dissipation system and the motor of the front-end prototype structure, the main reasons for heat dissipation faults are deeply analyzed. The method of statistical process control is utilized to predict the performance of the front-end motor and remind maintenance personnel to monitor the radar heat dissipation system in real-time. At the same time, by using the improved particle swarm optimization (PSO) algorithm model, the factors and kernel functions of the support vector machine (SVM) are optimized, and the regression accuracy of the SVM is improved. Furthermore, the motor failure prediction model is established, thereby ensuring the efficient and safe operating state of the radar system. The results show: (1) the failure of the radar motor is the major cause of heat dissipation faults; (2) compared to other algorithms, the efficiency of the PSO algorithm is improved by 30%, but the accuracy rate drops by 5%; (3) the applications of forewarning model for front-end prototype under statistical process control (SPC) can reduce the workload of maintenance personnel by 50%. The simulation results show that the combined method of SPC and SVM can predict the failure of the powering devices in radar heat dissipation systems. Besides, if the classification and regression models are combined, the difference between the predicted voltage and the true voltage will be smaller, and the accuracy will be higher. The above results provide a theoretical basis for the research of radar heat dissipation system and motor failure, which ensures the overall safety of the radar system and provides the necessary guarantee for the crew and the aviation command system.