Deep learning has achieved excellent results in many fields due to powerful feature extraction and learning ability. In this study, an improved method for analog circuit fault diagnosis based on a deep residual network is presented. The proposed method utilizes a ResNet to extract the performance characteristics of an analog circuit and determine the fault type of a component to realize the fault diagnosis of a circuit. The Short-time Fourier Transform is used to convert the time-domain output signals of a circuit into two-dimensional circuit spectrum maps, which are further used as the ResNet input. The fault diagnostic performance of the proposed method is verified by simulation with the Sallen-key bandpass filter circuit and the Four-opamp biquad high-pass filter circuit. The simulation results show that the proposed method performs well on both test circuits, achieving the diagnostic accuracy of up to 99.1% on the second-mentioned circuit.

中文翻译：

---

基于电路谱图的 ResNet 模拟电路软故障诊断

由于强大的特征提取和学习能力，深度学习在很多领域都取得了优异的成绩。在这项研究中，提出了一种基于深度残差网络的模拟电路故障诊断改进方法。所提出的方法利用ResNet提取模拟电路的性能特征并确定组件的故障类型以实现电路的故障诊断。短时傅立叶变换用于将电路的时域输出信号转换为二维电路频谱图，进一步用作 ResNet 输入。通过Sallen-key带通滤波器电路和四运放双二阶高通滤波器电路的仿真验证了所提出方法的故障诊断性能。