基于CBAM-CNN的模拟电路故障诊断研究
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作者单位:

兰州理工大学

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中图分类号:

TP273

基金项目:

国家自然科学基金项目 (61963025); 甘肃省教育厅: 优秀研究生“创新之星”项目 (2021CXZX-499); 甘 肃省高等学校创新基金项目 (2021A-027)


Research on CBAM-CNN based analog circuit fault diagnosis
Author:
Affiliation:

Lanzhou University of Technology

Fund Project:

The National Natural Science Foundation of China (61963025);Gansu Provincial Department of Education: excellent graduate

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    摘要:

    针对模拟电路的故障特征难以提取,导致模型计算量复杂,诊断准确率不够高的问题,提出了一种基于注意力机制和卷积神经网络(CBAM-CNN)的模拟电路故障诊断方法。首先,利用卷积核提取输入层的图片特征,同时在每个卷积层后面连接一个矫正线性单元(ReLU),并添加批归一化层(BN)解决内部协变量偏移的问题,以提高非线性模型表达能力。其次,在批归一化层后添加注意力机制模块(CBAM),提取重要的特征后连接池化层,降低网络计算复杂度,提高网络的准确率与效率。最后以Sallen-Key低通滤波器和二级四运放双二阶低通滤波器为研究对象,进行故障诊断实验验证。结果表明,本文方法能够有效提升诊断精度,能够实现所有故障的高难分类与定位.

    Abstract:

    The difficulty in extracting the fault features of analog circuit leads to complex calculation and poor precision with the model. A fault diagnosis method for analog circuits based on attention mechanism and convolutional neural network (CBAM -CNN) is proposed. Firstly, the image features of the input layer were extracted by using the convolution kernel. Followed by rectifying linear unit (ReLU) was connected behind each convolution layer, and a batch normalization (BN) layer was added to solve the problem of internal covariate migration, so as to improve the expression ability of the nonlinear model. Secondly, the convolutional block attention module (CBAM) was added after the batch normalization layer to extract the important features. After CBAM, the pooling layer is connected to reduce the computational complexity of the network and improve the accuracy and efficiency of the network. Finally, the Sallen-Key low-pass filter and the two-stage four-op amplifier double-order low-pass filter are taken as the research objects. The results of fault diagnosis experiments demonstrate that the proposed method can effectively improve the diagnosis accuracy and realize the classification and location of all faults with high difficulty.

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历史
  • 收稿日期:2021-06-26
  • 最后修改日期:2021-10-19
  • 录用日期:2021-10-27
  • 在线发布日期: 2021-11-08
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