小波-神经网络在模拟电路故障诊断中的应用

Applying Wavelet Transform and Neural Networks to Fault Diagnosis in Analog Circuits

小波变换具有时频局部化、多尺度分析等特性,而神经网络具有非线性映射、学习推理等优点,将二者结合起来,提出基于小波-神经网络的模拟电路故障诊断方法。采用正弦信号仿真模拟电路,应用小波变换对模拟电路响应的采样信号进行故障特征提取,建立故障字典,然后利用神经网络对各种状态下的特征向量进行分类决策,实现模拟电路的故障诊断。对标准电路仿真结果表明:该方法能够实现故障检测及定位,具有准确率高的特点。

Combining the time-frequency location and multiple-scale analyzation of wavelet transform （WT） with the nonlinear mapping and generalizing of neural networks, a method of fault diagnosis in analog circuits was proposed.Sinusoidal input to the analog circuit was simulated and its output was sampled in time domain to collect training data for neural networks. The collected data was preprocessed by WT to generate fault features and build a fault dictionary. Feature vectors under certain states could be classified using neural networks, and fault diagnosis in analog circuits was realized.Simulation results on benchmark circuits show that this scheme is feasible and has many powerful virtues, such as diagnosing and locating faults quickly and exactly,