粒子群优化算法下雷达散热系统性能及前端样机结构设计

Zhen Wang, Jinwen Zhou
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引用次数: 2

摘要

为弄清航空雷达在散热方面存在的问题,优化雷达设备的整体作战能力,提高航空雷达设备的安全性,在研究雷达散热系统结构的前提下,通过对雷达散热系统和前端样机结构电机的运行情况进行分析,深入分析了产生散热故障的主要原因。采用统计过程控制的方法预测前端电机的性能,提醒维护人员对雷达散热系统进行实时监控。同时,利用改进的粒子群优化(PSO)算法模型,对支持向量机(SVM)的因子和核函数进行优化,提高了支持向量机(SVM)的回归精度。建立了电机故障预测模型,保证了雷达系统高效、安全的运行状态。结果表明:(1)雷达电机的故障是造成散热故障的主要原因;(2)与其他算法相比,粒子群算法的效率提高了30%,但准确率下降了5%;(3)应用统计过程控制(SPC)下的前端样机预警模型,可使维修人员的工作量减少50%。仿真结果表明,SPC和SVM相结合的方法可以有效地预测雷达散热系统中供电器件的故障。此外,如果将分类模型与回归模型相结合,预测电压与真实电压之间的差异会更小,精度也会更高。以上结果为雷达散热系统和电机故障的研究提供了理论依据,保证了雷达系统的整体安全,为机组人员和航空指挥系统提供了必要的保障。
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The Performance of Radar Heat Dissipation System under Particle Swarm Optimization Algorithm and Structural Design of Front-end Prototype
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.
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