基于深度学习的模拟电路故障诊断算法

Fault diagnosis algorithm for analog circuits based on deep learning

针对模拟电路易发生故障且不易诊断的问题,提出了一种基于深度学习的模拟电路故障诊断算法。该算法首先将采样的原始数据制作成语音形式,然后通过时频域变化转化为语谱图,最后再将其送入VGG16模型中进行训练与测试。实验结果表明,该算法用于模拟电路故障诊断时能够识别的故障种类达到9种,同时准确度达到了100%,具有很强的电路故障诊断能力。

In order to solve the problem that the analog circuit is prone to failure and difficult to diagnose, the fault diagnosis algorithm for analog circuits based on deep learning is proposed. In the algorithm, the sampled raw data is converted into a phonetic form, and then it is transformed into speech spectrum by time-frequency domain change, finally it is sent into VGG16 model for training and testing. The experimental results show that the algorithm can identify nine kinds of fault types with 100% accuracy, which is proven to have a strong capability in fault diagnosis.