摘要
文中主要进行模拟电路故障特征提取与神经网络结合的故障诊断研究。提出改进的经验模态算法(EMD)的故障特征提取方法。先通过Pspice获取电路中的可测输出节点的电压作为故障特征提取的数据,并导入到MATLAB中,然后将数据进行EMD分解得到多个包含原数据信息的内在模函数(IMF),以此来构建有效的故障特征向量,进而将得到的故障特征向量送入BP神经网络构建训练集与测试集,最后完成故障诊断。从仿真结果得到,此方法获得了较高的故障诊断正确率。
In this paper,the fault diagnosis of analog circuit fault feature extraction combined with neural network is presented.An improved empirical mode decomposition algorithm（EMD） for fault feature extraction is proposed.First through the Pspice it obtains measurable output node voltage in the circuit as fault feature data,which is imported into MATLAB,and then the data is decomposed by EMD to obtain a plurality of data,which contains the original information of the intrinsic mode function（IMF）,in order to build effective fault feature vector,then the fault feature vector is sent into BP neural network to build the training set and test set,finally it completes fault diagnosis.The simulation results show that this method has a higher accuracy of fault diagnosis.
出处
《信息技术》
2017年第12期37-40,46,共5页
Information Technology
基金
江苏省自然科学基金(BK20151500)
作者简介
王震（1992-）,男,硕士研究生,研究方向为雷达系统、模拟电路故障诊断.
