气象雷达散热系统动力装置的性能研究

发布时间：2017-12-31 12:01

  本文关键词：气象雷达散热系统动力装置的性能研究　出处：《中国民航大学》2016年硕士论文　论文类型：学位论文

  更多相关文章： 电机 故障预测 粒子群 支持向量机

【摘要】：随着航空事业的发展,航空安全问题受到广泛关注,而机载设备的安全性和可靠性直接影响航空安全。机载气象雷达是机载航空电子系统的重要子系统,雷达的损坏极有可能导致飞机中途返航,影响航班行程,给航空公司和机场带来巨大损失,所以预测故障的发生时间以及趋势,提前对失效航空器件进行维修、替换显得尤为重要。气象雷达系统失效的主要成因是散热系统故障,AMETEK航空风扇作为散热系统的核心部件,由永久分相式电容电机驱动,电机运行状态异常成为影响气象雷达工作效能的重要原因。因此快速有效地对电容式电机进行故障预测具有现实意义。本文在研究散热系统结构特征的基础上,通过分析气象雷达散热系统和其动力装置电容式电机的故障机理,采用统计过程控制方法对电容式电机的性能进行预判,提醒维修人员对气象雷达散热系统进行实时监测。同时运用改进的粒子群优化算法模型,优化支持向量机的惩罚因子和核函数,提高支持向量机回归预测精度,再进一步将支持向量回归机和支持向量分类机相结合,对电容式电机的电压、电流数据进行回归预测,建立电机故障预测模型,从而实现电容式电机运行的状态预测。通过实验仿真和现场验证,结果表明,基于支持向量机的电机故障预测模型可对气象雷达散热系统动力装置电机的故障作出快速准确预测,以便保障气象雷达系统正常工作,对提高飞行安全有较好的实用价值。
[Abstract]:With the development of aviation industry, aviation safety has been paid more and more attention, and the safety and reliability of airborne equipment have a direct impact on aviation safety. Airborne weather radar is an important subsystem of airborne avionics system. Radar damage is likely to lead to the mid-way return of aircraft, affect the flight itinerary, and bring huge losses to airlines and airports, so predict the time and trend of the failure, and repair the failed aviation devices ahead of time. The main cause of the failure of meteorological radar system is that the AMETEK aeronautical fan, as the core part of the heat dissipation system, is driven by permanent phase separation capacitor motor. The abnormal operating state of the motor has become an important reason to affect the operational efficiency of meteorological radar. Therefore, it is of practical significance to predict the fault of capacitive motor quickly and effectively. This paper studies the structural characteristics of the heat dissipation system. By analyzing the fault mechanism of capacitive motor of meteorological radar heat dissipation system and its power device, the performance of capacitive motor is forecasted by statistical process control method. At the same time, the improved particle swarm optimization algorithm model is used to optimize the penalty factor and kernel function of support vector machine to improve the prediction accuracy of support vector machine regression. Furthermore, the support vector regression machine and the support vector classifier are combined to predict the voltage and current data of the capacitive motor, and the fault prediction model of the motor is established. In order to realize the state prediction of capacitive motor operation, the experimental simulation and field verification show that. The motor fault prediction model based on support vector machine (SVM) can predict the motor fault of the heat dissipation system of meteorological radar quickly and accurately, so as to ensure the normal operation of meteorological radar system. It has good practical value to improve flight safety.
【学位授予单位】：中国民航大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TN959.4
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本文编号：1359661