基于FrFT-FD和KPCA模拟电路故障特征提取方法

为获得有效的故障特征信息，提出一种基于分数阶傅里叶变换分形维的模拟电路故障特征提取方法。首先，把原始数据空间中的特征数据映射到不同的分数阶空间，分别计算不同分数阶次下故障响应信号的分形维数；然后，采用核主元分析进一步对候选特征实施降维；最后，将优化后的特征向量作为故障特征，利用神经网络进行分类诊断。仿真结果表明，本方法能很好地获取不同故障响应信号的细微差异，增强不同故障模式的可分性，提高故障诊断准确率。

New Method to Extract Analog Circuit Fault Features Based on FrFT-FD and KPCA

The extraction of fault features is one of the key technologies in analog circuit fault diagnosis. To acquire effective features, a method to extract the fault features based on fractional Fourier transform (FrFT) and fractal dimension (FD) is proposed. The original feature data is mapped to different fractional space and the FD is computed. Furthermore, the FrFT-FD feature is carried out for data dimensionality reduction by using kernel principal component analysis (KPCA). Finally, the optimized feature vector is diagnosed by neural network (NN). The simulation results show that the proposed method can acquire a subtle difference, enhance the separability of different fault modes, and improve the diagnostic accuracy.