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范海花, 尚玉玲. 基于二维卷积神经网络的模拟电路故障诊断方法[J]. 桂林电子科技大学学报, 2023, 43(6): 493-500. DOI: 10.3969/1673-808X.2022165
引用本文: 范海花, 尚玉玲. 基于二维卷积神经网络的模拟电路故障诊断方法[J]. 桂林电子科技大学学报, 2023, 43(6): 493-500. DOI: 10.3969/1673-808X.2022165
FAN Haihua, SHANG Yuling. An analog circuit fault diagnosis method based on two dimensional convolutional neural network[J]. Journal of Guilin University of Electronic Technology, 2023, 43(6): 493-500. DOI: 10.3969/1673-808X.2022165
Citation: FAN Haihua, SHANG Yuling. An analog circuit fault diagnosis method based on two dimensional convolutional neural network[J]. Journal of Guilin University of Electronic Technology, 2023, 43(6): 493-500. DOI: 10.3969/1673-808X.2022165

基于二维卷积神经网络的模拟电路故障诊断方法

An analog circuit fault diagnosis method based on two dimensional convolutional neural network

  • 摘要: 传统的基于机器学习的模拟电路故障诊断方法依赖复杂的信号处理技术和专业知识来进行故障特征提取,其故障诊断过程复杂。针对上述问题,提出一种基于二维卷积神经网络(2D-CNN)的模拟电路故障诊断方法,将被测电路的原始输出电压转换成故障灰度图(fault gray image, 简称FGI),作为2D-CNN模型的输入,使用模型的卷积层自动提取故障的深层特征,并在模型中通过添加批量归一化(Batch Normalization, 简称BN)层对数据分布进行正则化,以减小数据分布偏移带来的影响。该方法在Sallen-Key 带通滤波器电路和四阶二运放高通滤波器电路的故障诊断实验中分别实现了100%和99.46%的故障诊断率。该方法不仅简化了故障诊断流程,还保证了故障诊断精度,并具有较强的泛化能力。

     

    Abstract: Analog circuit fault diagnosis based on traditional machine learning method relies on complex signal processing technology and professional knowledge for fault feature extraction, and the process of fault diagnosis is complex. In view of this, an analog circuit fault diagnosis method based on two dimensional convolutional neural network (2D-CNN) was proposed. The original output voltage of the circuit under test was converted into fault gray image (fault gray image, FGI) as the input of 2D-CNN model. The convolution layer of the model was used to automatically extract the deep features of the fault, and the batch normalization (Batch Normalization, BN) layer was added to the model to regularize the data distribution, so as to alleviate the impact of data distribution offset. In the fault diagnosis experiments of Sallen-key bandpass filter circuit and Four-opamp biquad high-pass filter circuit, the fault diagnosis rates achieve by the proposed method are 100% and 99.46% respectively. The proposed method not only simplifies the fault diagnosis process, but also ensures the accuracy of fault diagnosis, and has strong generalization ability.

     

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