神经网络自学习下模拟电路故障诊断技术研究  

Research on Fault Diagnosis Technology for Analog Circuits under Self Learning of Neural Network

作　　者：王俊亚 祝丰菊 WANG Junya;ZHU Fengju(Hubei Industrial Polytechnic,Shiyan 442000,China)

机构地区：[1]湖北工业职业技术学院,湖北十堰442000

出　　处：《通信电源技术》2025年第4期218-220,共3页Telecom Power Technology

摘　　要：为解决传统模拟电路故障诊断中错误高、用时长等问题,提出一种面向模拟电路故障诊断的技术优化方法。通过等效采集双端口网络运行数据,提取时域和频域特征,基于神经网络自学习对电路故障做出诊断和识别。通过实验对比分析,该技术优化方法在诊断时长、诊断正确率等方面具有良好表现。In order to solve problems such as high error and time spent in traditional analog circuit fault diagnosis,a technical optimization method for analog circuit failure diagnosis is proposed.By equally collecting the operational data of the dual port network,extracting temporal and frequency domain characteristics,and diagnosing and identifying circuit failures based on neural network self-learning.Through experimental comparative analysis,the technical optimization method performed well in terms of diagnostic duration and diagnostic correctness.

关 键 词：模拟电路 故障诊断技术 神经网络自学习 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]