联合多FRFT域信息熵的模拟电路Iddt故障诊断  

Analog circuit Iddt fault diagnosis combining information entropy in multi⁃FRFT domain

作　　者：余坚铿 张超杰[1] 吴杰长[1] YU Jiankeng;ZHANG Chaojie;WU Jiechang(College of Power Engineering,Naval University of Engineering,Wuhan 430033,China)

机构地区：[1]海军工程大学动力工程学院,湖北武汉430033

出　　处：《现代电子技术》2020年第18期92-96,共5页Modern Electronics Technique

基　　金：国家自然科学基金项目:基于动态电源电流的舰船动力监控模拟电路故障诊断(51509255)。

摘　　要：电路集成化的不断提高使得可及测点越来越少,有限的可测信号限制了模拟电路故障诊断技术的发展。对此,将新的测试信号,即动态电源电流(Iddt)信号应用于模拟电路故障诊断中。测取待诊断电路(CUT)的Iddt信号并进行分数阶傅里叶变换(FRFT),提取不同FRFT域内的信息熵作为故障特征值,通过核主元分析(KPCA)进行特征降维,输入概率神经网络(PNN)进行分类。仿真实验考虑了电路的单双故障类型,结果表明,文中方法的诊断性能高于其他参比模型。With the continuous improvement of circuit integration,the accessible measurement points are less and less,and the limited measurable signals limit the development of fault diagnosis technology of analog circuits.Therefore,the new test signal(dynamic supply current(Iddt)signal)is applied to analog circuit fault diagnosis.The Iddt signal of the circuit under test(CUT)is detected for the fractional Fourier transform(FRFT).The information entropy in different FRFT domains is extracted as fault eigenvalue.The feature dimensionality reduction is carried out by kernel principal component analysis(KPCA).The faults are classified by inputting probabilistic neural network(PNN).The type of single and double faults of the circuit was considered in the simulation experiment.The results show that the diagnostic performance of the method is higher than that of other reference models.

关 键 词：故障诊断 信息熵提取 动态电源电流 特征降维 分数阶傅里叶变换 仿真实验 

分 类 号：TN710.4-34[电子电信—电路与系统] TP277[自动化与计算机技术—检测技术与自动化装置]