基于深度残差网络的模拟电路软故障诊断方

航天电子电路的故障诊断一直是航天领域可靠性与安全性的一个热点研究课题，航天电子电路的故障将直接影响航天任务是否成功。提出了一种基于深度残差网络的模拟电路故障诊断方法。该方法使用短时傅里叶变换将电路的时域输出信号转换为二维电路图像，并将其作为神经网络的输入，再利用ResNet提取模拟航天电子电路的性能特征，确定元件的故障类型，完成电路的故障诊断。通过仿真验证了该方法的故障诊断性能。仿真结果表明，该方法能够实现高达99.1%的诊断准确率。

Deep Residual Learning-Based Soft Fault Diagnosis Method for Analog Circuits

The fault diagnosis of aerospace electronic circuits has been a hot research topic in the field of space reliability. The failure of space electronic circuits can directly affect the success of space missions. 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. And the Short-time Fourier Transform is used to convert the time-domain output signals of a circuit into two-dimensional circuit images, which are further used as the ResNet input. The fault diagnostic performance of the proposed method is verified by simulation and the results show that the proposed method can achieve the diagnostic accuracy of up to 99.1%.