基于神经网络的射频元器件故障智能检测方法  

Intelligent Fault Detection Method for Radio Frequency Component Based on Neural Network

作　　者：张超 ZHANG Chao(Shanxi College of Applied Science and Technology,Taiyuan 030062)

机构地区：[1]山西应用科技学院,太原030062

出　　处：《现代制造技术与装备》2025年第1期156-158,共3页Modern Manufacturing Technology and Equipment

摘　　要：在射频元器件故障智能检测中,传统方法应用效果不佳,不仅检测准确性较低,而且检测时效性较差,无法达到预期效果。为解决这一问题,提出基于神经网络的射频元器件故障智能检测方法。将射频元器件负载端输出电压和输入端电流作为采样点,利用无线传感器捕获射频元器件故障数据,采用最大值最小值法对这些数据进行归一化处理,以消除数据量纲。然后利用神经网络处理和分析数据,识别射频元器件故障的类型,实现故障智能检测。经实验验证,文章方法的检测结果与实际交并比在0.970以上,检测耗时不超过10ms,具有较高的准确性与时效性,在射频元器件故障智能检测领域具有很好的应用前景。In the intlligent fault detection of radio frequency components,the application effect of traditional methods is not good,not only the detection accuracy is low,but also the detection timeliness is poor,and can not achieve the expected effect.To solve this problem,an intelligent fault detection method of radio frequency components based on neural network is proposed.The output voltage and input current of radio frequency components at the load end are taken as sampling points,and the fault data of radio frequency components are captured by wireless sensors,and the data is normalized by the max-min method to eliminate the data dimension.Then,the neural network is used to process and analyze the data,identify the type of fault of the radio frequency components,and realize the intelligent fault detection.The experimental results show that the intersection ratio between the detection results and the actual results is more than 0.970,and the detection time is less than 10 ms,which has high accuracy and timeliness,and has a good application prospect in the field of intelligent fault detection of radio frequency components.

关 键 词：射频元器件 故障智能检测 神经网络 

分 类 号：TN9[电子电信—信息与通信工程]