基于深度学习的模拟电路故障诊断方法研究  

Research on Fault Diagnosis Method for Analog Circuits Based on Deep Learning

作　　者：蔡金苹[1] CAI Jinping(Shenyang Workers University,Shenyang Liaoning 110000)

机构地区：[1]沈阳职工大学,辽宁沈阳110000

出　　处：《中国科技纵横》2024年第16期52-54,共3页China Science & Technology Overview

摘　　要：本文重点研究基于深度学习的模拟电路故障诊断方法。基于传统故障诊断方法的分析以及深度学习技术在其他领域的成功应用,提出了一种基于深度学习的模拟电路故障诊断方法。通过构建合适的数据集、设计合适的神经网络模型以及优化训练算法,实现对模拟电路故障的准确诊断。该方法能够有效提高故障诊断的准确性和效率,具有广阔的应用前景。This paper aims to study a fault diagnosis method for analog circuits based on deep learning.Through the analysis of traditional fault diagnosis methods and the successful application of deep learning technology in other fields,a deep learning based analog circuit fault diagnosis method is proposed.By constructing appropriate datasets,designing appropriate neural network models,and optimizing training algorithms,accurate diagnosis of faults in analog circuits can be achieved.This method can effectively improve the accuracy and efficiency of fault diagnosis,and has broad application prospects.

关 键 词：深度学习 模拟电路 故障诊断 

分 类 号：TN710[电子电信—电路与系统]