基于Treelet变换的模拟电路故障诊断

针对模拟电路故障诊断中特征向量冗余的问题,提出一种基于Treelet变换的模拟电路故障诊断方法.Treelet变换是一种自适应的多尺度的数据分析方法,适用于对高维数据降维和特征选择。文中首先对被测电路的输出信号采样,将采集到的信号进行Treelet变换,提取故障特征向量,最后将得到的特征向量输入BP神经网络进行故障模式识别。仿真实验结果表明,该方法能够有效地提取电路故障特征。与其他故障特征提取方法相比较,基于Treelet变换的模拟电路故障诊断方法具有较高的故障诊断率和收敛速度。

LIU Mei-rong ,LI Lang

In order to solve the problem in the feature extraction of analog circuit, a method based on Treelet transform is proposed in this paper. Treelet transform is an adaptive multi-scale data analysis method that can be used in the high dimensional data dimension reduction and feature selection.This approach performs Treelet transform on the acquired output signals of the test circuit as the fault feature vector.Then the obtained feature vector is used as the input of the BPNN to recognize the fault patterns. The simulation result showed that the proposed approach can effectively extract the fault feature vector. Compared with other feature extraction methods, Treelet transform has good accuracy of analog circuit fault diagnosis.