基于故障树模型的航空雷达在线入侵诊断系统设计

面对传统航空雷达在线入侵诊断系统受到刷新率影响，导致诊断精准度较低的问题，提出了基于故障树模型的航空雷达在线入侵诊断系统设计。使用FS-N40型号光纤传感器，将待测物理量转换为相位或振幅形式，由此获取被测参数。采用DS1102Z-E数字示波器，兼顾波形全局和细节不漏失情况，随时刷新波形，避免故障情况发生。选择S2型号红外摄像仪，导出诊断结果。构建雷达层次故障树模型，确定故障特征向量和功能子单元，缩小死区范围。结合割集结构函数，设计在线入侵诊断流程。由实验结果可知，设计系统最高诊断精准度为0.95，能够准确诊断航空雷达在线入侵行为，为保障航空航天事业快速发展提供设备支持。

Design of Online Intrusion Diagnosis System for Aviation Radar Based on Fault Tree Model

Facing the problem that the traditional airborne radar online intrusion diagnosis system is affected by the refresh rate, resulting in low diagnosis accuracy, a design of airborne radar online intrusion diagnosis system based on fault tree model is proposed. FS-N40 optical fiber sensor is used to convert the physical quantity to be measured into phase or amplitude form, so as to obtain the measured parameters. DS1102Z-E digital oscilloscope is adopted, taking into account the overall situation and details of the waveform without leakage, and refreshing the waveform at any time to avoid failure. Select S2 infrared camera and export the diagnosis results. The radar hierarchical fault tree model is constructed to determine the fault feature vector and functional sub units to reduce the dead zone range. Combined with the cut set structure function, the online intrusion diagnosis process is designed. According to the experimental results, the highest diagnostic accuracy of the designed system is 0.95, which can accurately diagnose the online intrusion of airborne radar and provide equipment support for the rapid development of aerospace industry.