基于FrFT-FD和KPCA模拟电路故障特征提取方法  

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

作　　者：周绍磊[1] 廖剑[1] 史贤俊[1] 

机构地区：[1]海军航空工程学院控制工程系,烟台264001

出　　处：《振动．测试与诊断》2014年第2期337-344,400-401,共8页Journal of Vibration,Measurement & Diagnosis

基　　金：国家青年科学基金资助项目(61203168)

摘　　要：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。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 di-agnosed 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.

关 键 词：模拟电路 故障诊断 分数阶傅里叶变换 分形维数 核主元分析 

分 类 号：TP206.1[自动化与计算机技术—检测技术与自动化装置] TN707[自动化与计算机技术—控制科学与工程]