基于时间卷积网络的模拟电路故障诊断方法

对模拟电路故障诊断中的故障特征提取进行了研究，引入了卷积神经网络模型，提出了一种基于时间卷积神经网络的模拟电路故障诊断方法，在四阶Butterworth低通滤波器电路上分别对不同深度的时间卷积网络进行分类对比实验，实验结果证实了深度时间卷积网络在故障特征提取中的有效性。同时设计实验对比了时间卷积神经网络、SAE-SOFTMAX、深度信念网络和长短期记忆网络的特征提取能力，结果表明，时间卷积神经网络模型在模拟电路故障诊断中能够提取出更能反映数据本质的特征，取得更好的诊断准确率。

Fault diagnosis method of analog circuits based on temporal CNN

In view of the feature extraction for analog circuit fault diagnosis, this paper introduces the convolutional neural network into this study field, and presents an analog circuit fault diagnosis method based on deep temporal convolutional neural network (TCN). Comparison experiments on a 4th-order Butterworth lowpass filter with a group of TCN with various depth prove that the model of deep TCN is effective in feature extraction of circuit fault. Moreover, comparison experiments with a group of models including TCN, SAE-SOFTMAX, deep belief network and long short-term memory network prove that the TCN based fault diagnosis method is more effective to extract features closed to essence of data, and capable to achieve a better accuracy in analog circuit fault diagnosis.