Fault diagnosis network parameters setting.

To accurately locate faulty components in analog circuits, an analog circuit fault diagnosis method based on Tunable Q-factor Wavelet Transform(TQWT) and Convolutional Neural Network (CNN) is proposed in this paper. Firstly, the Grey Wolf algorithm (GWO) is used to improve the TQWT. The improved TQWT can adaptively determine the parameters Q-factor and decomposition level. Secondly, The signal is decomposed, and single-branch reconstruction is conducted with TQWT to facilitate adequate feature extraction. Thirdly, to capture the time-frequency features in the signal, a CNN-LSTM network is built by combining CNN and LSTM for feature extraction. Finally, CNN, which introduces Fully Convolutional Network (FCN) layers and a Batch Normalization layer, is used to fault diagnosis. The method was comprehensively evaluated with a second-order bandpass filter circuit. The experimental results illustrate that the proposed fault diagnosis method can achieve excellent fault diagnosis accuracy, and the average accuracy is 98.96%.

针对模拟电路故障元件的精准定位问题，本文提出一种基于可调Q因子小波变换（Tunable Q-factor Wavelet Transform，TQWT）与卷积神经网络（Convolutional Neural Network，CNN）的模拟电路故障诊断方法。首先，采用灰狼优化算法（Grey Wolf algorithm，GWO）对TQWT进行改进，改进后的TQWT可自适应确定Q因子与分解层数参数。其次，通过TQWT对信号进行分解并执行单分支重构，以实现充分的特征提取。再者，为捕捉信号中的时频特征，构建结合卷积神经网络与长短期记忆网络（Long Short-Term Memory，LSTM）的CNN-LSTM网络用于特征提取。最后，引入全卷积网络（Fully Convolutional Network，FCN）层与批量归一化（Batch Normalization）层的卷积神经网络用于故障诊断。本文采用二阶带通滤波电路对所提方法进行全面评估，实验结果表明，所提出的故障诊断方法可实现优异的故障诊断准确率，平均准确率达98.96%。