小波变换和神经网络的电路故障诊断

Circuit fault diagnosis based on wavelet transform and neural network

模拟电路的元件较多,相互之间的耦合性较强,容易发生故障。为了提高电路故障的诊断准确性,提出小波变换和神经网络的电路故障诊断方法。首先采用小波变换方法提取电路故障信息特征,然后采用神经网络分类提取的故障特征量实现对电路故障的诊断和分类识别,最后通过仿真实验进行性能测试,结果表明,该方法提高了电路故障检测的准确度,具有较好的实际应用价值。

The multiple components of the analog circuit have strong coupling among them, and it is prone to failure. In order to improve the accuracy of the circuit fault diagnosis, a circuit fault diagnosis method based on wavelet transform and neural network is proposed. The wavelet transform method is used to extract the information feature of the circuit fault. The neural net- work is used to classify and extract the fault feature quantity to realize the circuit fault diagnosis, and classification and recogni- tion. The performance was tested with the simulation experiment. The results show that the method has improved the accuracy of the circuit fault detection, and has superior practical application value.