基于深度学习的复杂模拟电路故障识别  

Fault Identification of Complex Analog Circuit Based on Deep Learning

作　　者：黄泽华 毕贵红[1] 张梓睿 HUANG Zehua;BI Guihong;ZHANG Zirui(Faculty of Electric Power Engineering,Kunming University of Science and Technology,Kunming 650500,China;Yongzhou Industrial Trade Secondary Professional School,Yongzhou 425300,China)

机构地区：[1]昆明理工大学电力工程学院,云南昆明650500 [2]永州市工业贸易中等专业学校,湖南永州425300

出　　处：《电子科技》2025年第2期23-34,共12页Electronic Science and Technology

基　　金：云南省科技厅科技计划(202201AT070155)。

摘　　要：复杂的模拟电路故障传递关系复杂,故障类型与故障特征之间存在复杂的非线性关系,导致特征提取困难和故障识别困难。针对该问题,文中提出了一种两测点、自适应噪声完备集合经验模态分解(Complete Ensemble Empirical Mode Decomposition with Adaptive Noise,CEEMDAN)-多尺度伪彩色图像-AlexNet网络结合的模拟电路故障诊断方法。将复杂模拟电路两测点输出信号前后相接构造新的组合故障样本,两测点组合故障样本数据提高了表征复杂模拟电路整体故障状态的能力。将组合的故障样本信号进行多尺度分解,并将多尺度数据矩阵映射为二维伪彩色图,形成信息丰富、特征明显的多尺度故障伪彩色图像。利用深度学习模型AlexNet优异的图像特征挖掘和学习能力,将不同故障类型的多尺度伪彩色图像输入AlexNet中进行模型迁移训练学习并完成故障识别。通过对简单电路和复杂电路的单双故障及混合故障识别的比较分析,证明了所提模拟电路故障诊断方法对复杂模拟电路的不同故障类型能达到更高的识别准确率。For complex analog circuits with complex fault transfer relationships and complex nonlinear relationships between fault types and fault features,which cause difficulties in feature extraction and fault identification.This study presents a fault diagnosis method for analog circuits based on two measuring points-CEEMDAN(Complete Ensemble Empirical Mode Decomposition with Adaptive Noise)-multi-scale false-color image-ALEXNet network.A new combined fault sample is constructed by connecting the output signals of two measuring points in a complex analog circuit.The combined fault sample data of two measuring points improves the ability to characterize the whole fault state of the complex analog circuit.The combined fault sample signals are decomposed in multi-scale,and the multi-scale data matrix is mapped to a two-dimensional false-color graph to form a multi-scale false-color image with abundant information and obvious features.Based on AlexNet's excellent image feature mining and learning ability,multi-scale false-color images of different fault types were input into AlexNet for model transfer training and fault identification.By comparing and analyzing the single and double faults and mixed faults of simple circuit and complex circuit,it is proved that the proposed method can achieve higher recognition accuracy for different fault types of complex analog circuit.

关 键 词：模拟电路 故障诊断 双故障 CEEMDAN 伪彩色 AlexNet 低通滤波器 特征可视化 

分 类 号：TN710[电子电信—电路与系统]