电子测量与仪器学报

刘美容,曾黎,何怡刚,李向新.基于LMD多尺度熵和极限学习机的模拟电路故障诊断[J].电子测量与仪器学报,2017,31(4):530-536

基于LMD多尺度熵和极限学习机的模拟电路故障诊断

Analog circuit fault diagnosis based on LMD multi scale entropy and extreme learning machine

DOI：10.13382/j.jemi.2017.04.006

英文关键词:LMD extreme learning machine multi scale entropy fault diagnosis feature vector extraction

基金项目:国家自然科学基金（51577046）、国家自然科学基金重点项目（51637004）、国家重点研发计划“重大科学仪器设备开发”项目（2016YFF0102200）、湖南省教育厅项目（17C0956）资助

作者	单位
刘美容	1．湖南师范大学物理与信息科学学院长沙410081；２．合肥工业大学电气工程博士后流动站合肥230009
曾黎	湖南师范大学物理与信息科学学院长沙410081
何怡刚	合肥工业大学电气与自动化工程学院合肥 230009
李向新	国网湖南省邵阳供电公司邵阳422000

Author	Institution
Liu Meirong	1. College of Physics and Information Science, Hunan Normal University, Changsha 410081, China;2. Electric Engineering Postdoctoral Center, Hefei University of Technology, Hefei 230009, China;
Zeng Li	College of Physics and Information Science, Hunan Normal University, Changsha 410081, China
He Yigang	College of Electrical and Automation Engineering, Hefei University of Technology, Hefei 230009, China
Li Xiangxin	;4. State Grid Shaoyang Power Supply Company, Shaoyang 422000, China

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中文摘要:

为了高速、高效的测试和诊断模拟电路，提出一种将局部均值分解(LMD)多尺度熵和极限学习机相结合的模拟电路故障诊断的新方法。该方法中，首先采用LMD将故障信号分解为若干个乘积函数（production function,PF）；然后，求出各PF分量的多尺度熵并构造故障特征向量；最后，将特征向量输入到极限学习机中进行训练和测试。仿真实验结果显示采用该方法诊断时间只需0.028 74 s，诊断精度达到了98.89%。相较于其他3种方法有效减少诊断时间，提高故障诊断精度。

英文摘要:

In order to efficiently test and high speed diagnose analog circuits, a new analog circuit fault diagnosis method based on LMD multi scale entropy and extreme learning machine is proposed in this paper. First, the fault signal is decomposed into several production functions by LMD. Then, the multi scale entropy of each PF component is worked out and fault feature vectors are constructed. Finally, the fault feature vectors are feed into the extreme learning machine to train and test. The simulation results show that the diagnosis time only needs 0.028 74 s, and the accuracy of fault diagnosis can achieve 98.89%. Compared with other three ways, the method can effectively reduce the diagnosis time and improve the accuracy of fault diagnosis.

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