College of Aviation, Zhongyuan University of Technology, Zhengzhou 451191, China.
School of Computer and Information Science and Institute for AI Industrial Technology Research, Hubei Engineering University, Xiaogan, Hubei 432000, China.
Rev Sci Instrum. 2023 May 1;94(5). doi: 10.1063/5.0142657.
In this work, an effective approach based on a nonlinear output frequency response function (NOFRF) and improved convolution neural network is proposed for analog circuit fault diagnosis. First, the NOFRF spectra, rather than the output of the system, are adopted as the fault information of the analog circuit. Furthermore, to further improve the accuracy and efficiency of analog circuit fault diagnosis, the batch normalization layer and the convolutional block attention module (CBAM) are introduced into the convolution neural network (CNN) to propose a CBAM-CNN, which can automatically extract the fault features from NOFRF spectra, to realize the accurate diagnosis of the analog circuit. The fault diagnosis experiments are carried out on the simulated circuit of Sallen-Key. The results demonstrate that the proposed method can not only improve the accuracy of analog circuit fault diagnosis, but also has strong anti-noise ability.
在这项工作中，提出了一种基于非线性输出频率响应函数（NOFRF）和改进卷积神经网络的有效方法，用于模拟电路故障诊断。首先，采用 NOFRF 谱而不是系统的输出作为模拟电路的故障信息。此外，为了进一步提高模拟电路故障诊断的准确性和效率，将批量归一化层和卷积注意力模块（CBAM）引入卷积神经网络（CNN）中，提出了 CBAM-CNN，它可以自动从 NOFRF 谱中提取故障特征，从而实现对模拟电路的准确诊断。在 Sallen-Key 的模拟电路上进行了故障诊断实验。结果表明，所提出的方法不仅可以提高模拟电路故障诊断的准确性，而且具有很强的抗噪声能力。
