| 基于EEMD-Renyi熵和PCA-PNN的滚动轴承故障诊断 |
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| 引用本文： | 窦东阳,李丽娟,赵英凯.基于EEMD-Renyi熵和PCA-PNN的滚动轴承故障诊断[J].东南大学学报(自然科学版),2011,41(Z1):107-111. |
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| 作者姓名： | 窦东阳 李丽娟 赵英凯 |
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| 作者单位： | 1. 中国矿业大学化工学院,徐州,221116
2. 南京工业大学自动化与电气工程学院,南京,210009 |
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| 基金项目： | 江苏省自然科学基金资助项目(BK2009356); 江苏省高校自然科学研究资助项目(09KJB510003) |
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| 摘 要： | 针对滚动轴承故障特征提取与状态监测问题,提出一种基于集合经验模式分解(EEMD)、Renyi熵、主元分析(PCA)和概率神经网络(PNN)的新方法.首先,将轴承振动信号通过EEMD分解成一组本征模态函数(IMF),计算每个IMF分量的Renyi熵值作为表征故障特征的向量,采用主元分析(PCA)对特征降维,提取主元输入概...
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| 关 键 词： | 故障诊断 滚动轴承 集合经验模式分解 Renyi熵 主元分析 概率神经网络 |
Fault diagnosis of rolling bearings using EEMD-Renyi entropy and PCA-PNN |
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| Dou Dongyang,Li Lijuan,Zhao Yingkai.Fault diagnosis of rolling bearings using EEMD-Renyi entropy and PCA-PNN[J].Journal of Southeast University(Natural Science Edition),2011,41(Z1):107-111. |
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| Authors: | Dou Dongyang Li Lijuan Zhao Yingkai |
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| Institution: | Dou Dongyang1 Li Lijuan2 Zhao Yingkai2 (1 School of Chemical Engineering and Technology,China University of Mining and Technology,Xuzhou 221116,China) (2 School of Automation and Electrical Engineering,Nanjing University of Technology,Nanjing 210009,China) |
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| Abstract: | In order to solve the problems of fault feature extraction and condition monitoring of rolling bearings,a novel approach based on the ensemble empirical mode decomposition(EEMD),the Renyi-entropy,the principal component analysis(PCA) and the probabilistic neural network(PNN) is proposed.The vibration signals are first decomposed into a couple of intrinsic mode functions(IMFs) by the EEMD method,and the Renyi entropy of each IMF is computed as the fault characteristic vectors.Then,the PCA is used for feature... |
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| Keywords: | fault diagnosis rolling bearing ensemble empirical mode decomposition(EEMD) Renyi entropy principal component analysis(PCA) probabilistic neural network(PNN) |
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