计算机工程与科学2025，Vol.47Issue(1)：35-44,10.DOI:10.3969/j.issn.1007-130X.2025.01.005
基于Patches-CNN的模拟电路故障诊断
Fault diagnosis of analog circuits based on Patches-CNN
摘要
Abstract
Deep learning is widely used in fault diagnosis,but currently,deep learning-based fault di-agnosis models for analog circuits are relatively complex and difficult to deploy on edge devices.To ad-dress this issue and further improve fault diagnosis accuracy,a simple and lightweight deep learning model for analog circuit fault diagnosis,named Patches-CNN,is proposed.Firstly,the input image is divided into patches and transformed into word vectors(tokens)through a Patch Embedding operator,serving as the input for a ViT-style homogeneous structure.Feature extraction and information acquisi-tion among tokens are carried out using the lightweight operator GSConv,which can effectively enhance the fault diagnosis accuracy of the model.Secondly,layer normalization is added to prevent gradient ex-plosion and accelerate model convergence.To increase the nonlinearity of the model,the GELU activa-tion function is employed.Finally,the Sallen-Key band-pass filter circuit and the Four-Opamp biquad high-pass filter circuit are used as experimental subjects.Experimental results demonstrate that this model can achieve accurate fault classification and location.关键词
模拟电路/故障诊断/深度学习/同质结构/层归一化Key words
analog circuit/fault diagnosis/deep learning/homogeneous structure/layer normalization分类
信息技术与安全科学引用本文复制引用
吴玉虹,王建..基于Patches-CNN的模拟电路故障诊断[J].计算机工程与科学,2025,47(1):35-44,10.基金项目
国家自然科学基金(62162034) （62162034）
