| 基于LMD多尺度熵和极限学习机的模拟电路故障诊断 |
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| 引用本文： | 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. |
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| 作者姓名： | 刘美容 曾黎 何怡刚 李向新 |
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| 作者单位： | 1. 湖南师范大学物理与信息科学学院 长沙 410081;合肥工业大学电气工程博士后流动站 合肥 230009;2. 湖南师范大学物理与信息科学学院 长沙 410081;3. 合肥工业大学电气与自动化工程学院 合肥 230009;4. 国网湖南省邵阳供电公司 邵阳 422000 |
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| 基金项目： | 国家自然科学基金，国家自然科学基金重点项目，国家重点研发计划"重大科学仪器设备开发"项目，湖南省教育厅项目 |
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| 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。
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| 关 键 词： | 局部均值分解 极限学习机 多尺度熵 故障诊断 特征向量提取 |
Analog circuit fault diagnosis based on LMD multi scale entropy and extreme learning machine |
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| Liu Meirong,Zeng Li,He Yigang and Li Xiangxin.Analog circuit fault diagnosis based on LMD multi scale entropy and extreme learning machine[J].Journal of Electronic Measurement and Instrument,2017,31(4):530-536. |
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| Authors: | Liu Meirong Zeng Li He Yigang and Li Xiangxin |
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| Affiliation: | 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;,College of Physics and Information Science, Hunan Normal University, Changsha 410081, China,College of Electrical and Automation Engineering, Hefei University of Technology, Hefei 230009, China and ;4. State Grid Shaoyang Power Supply Company, Shaoyang 422000, China |
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| Abstract: | 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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| Keywords: | LMD extreme learning machine multi-scale entropy fault diagnosis feature vector extraction |
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