基于Patches-CNN的模拟电路故障诊断  

Fault diagnosis of analog circuits based on Patches-CNN

作　　者：吴玉虹[1] 王建 WU Yuhong;WANG Jian(Faculty of Information Engineering and Automation,Kunming University of Science and Technology,Kunming 650504,China)

机构地区：[1]昆明理工大学信息工程与自动化学院,云南昆明650504

出　　处：《计算机工程与科学》2025年第1期35-44,共10页Computer Engineering & Science

基　　金：国家自然科学基金(62162034)。

摘　　要：深度学习在故障诊断中应用广泛,但目前基于深度学习的模拟电路故障诊断模型复杂度较高,难以在边缘设备上部署。针对该问题,为了进一步提高故障诊断精度,提出了一种简单且轻量化的Patches-CNN模拟电路故障诊断深度学习模型。首先,将输入的图像分割成patches,并通过Patches Embedding算子转换为词向量(tokens),作为ViT风格的同质结构的输入,利用轻量化算子GSConv进行特征提取和获取token之间的信息,可以有效地提高模型的故障诊断精度。其次,添加层归一化可以防止模型梯度爆炸和加快模型收敛,为了提升模型的非线性,采用GELU激活函数。最后,将Sallen-Key带通滤波电路和Four-Opamp双二阶高通滤波电路作为实验对象。实验结果表明,该模型可以实现故障的准确分类与定位。Deep learning is widely used in fault diagnosis,but currently,deep learning-based fault diagnosis models for analog circuits are relatively complex and difficult to deploy on edge devices.To address 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 acquisition 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 explosion and accelerate model convergence.To increase the nonlinearity of the model,the GELU activation 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 

分 类 号：TN406[电子电信—微电子学与固体电子学]