Study on Fault Diagnosis for Bearing Based on VMD-SVD and Extreme Learning Machine

机译：基于VMD-SVD和极限学习机的轴承故障诊断研究

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Bearings are key components in many mechanical facilities, and the research on fault diagnosis for bearing is of great importance to the safe operation of those facilities. Thus, a method for fault diagnosis based on VMD-SVD and extreme learning machine is proposed in this paper. First, the bearing vibration signal is decomposed into a number of stationary intrinsic mode functions (IMF) by VMD method. Second, the initial feature matrix of each IMF component is decomposed by SVD, and the obtained singular value is used as the eigenvector of the signal. Finally, extreme learning machine is used as the classifier for fault diagnosis. This method's feasibility and effectiveness have also been verified by experiment.

机译：轴承是许多机械设备中的关键部件，轴承故障诊断的研究对于这些设备的安全运行非常重要。因此，本文提出了一种基于VMD-SVD和极限学习机的故障诊断方法。首先，通过VMD方法将轴承振动信号分解为多个固定的固有模式函数（IMF）。其次，将每个IMF分量的初始特征矩阵通过SVD分解，并将获得的奇异值用作信号的特征向量。最后，极限学习机被用作故障诊断的分类器。实验还验证了该方法的可行性和有效性。

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来源
《Proceedings of the 3rd international conference on electrical and information technologies for rail transportation: transportation》|2017年|87-97|共11页
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作者
Qiang Zhou; Yong Qin; Zhipeng Wang; Limin Jia;
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作者单位

State Key Lab of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China;

State Key Lab of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China;

State Key Lab of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China;

State Key Lab of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China;

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原文格式 PDF
正文语种 eng
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关键词
Fault diagnosis; Variation mode decomposition; Singular value decomposition; Extreme learning machine;

机译：故障诊断；变化模式分解；奇异值分解;极限学习机;

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1. A Novel Faults Diagnosis Method for Rolling Element Bearings Based on ELCD and Extreme Learning Machine [J] . Liang Mingliang, Su Dongmin, Hu Daidi, Shock and vibration . 2018,第PTa1期

机译：基于ELCD和极限学习机的滚动轴承故障诊断新方法。
2. A Novel Faults Diagnosis Method for Rolling Element Bearings Based on ELCD and Extreme Learning Machine [J] . Mingliang Liang, Dongmin Su, Daidi Hu, Shock and vibration . 2018,第1期

机译：基于ELCD和极端学习机的滚动元件轴承的新型故障诊断方法
3. Research on Novel Bearing Fault Diagnosis Method Based on Improved Krill Herd Algorithm and Kernel Extreme Learning Machine [J] . Zhijian Wang, Likang Zheng, Junyuan Wang, Complexity . 2019,第1期

机译：基于改进的KRILL群算法和内核极端学习机的新型轴承故障诊断方法研究
4. Study on Fault Diagnosis for Bearing Based on VMD-SVD and Extreme Learning Machine [C] . Qiang Zhou, Yong Qin, Zhipeng Wang, International conference on electrical and information technologies for rail transportation: transportation . 2018

机译：基于VMD-SVD和极端学习机的轴承故障诊断研究
5. Internal Fault Diagnosis of MMC-HVDC Based on Classification Algorithms in Machine Learning [D] . Jin, Tianyi. 2019

机译：基于机器学习分类算法的MMC-HVDC的内部故障诊断
6. A Personalized Diagnosis Method to Detect Faults in a Bearing Based on Acceleration Sensors and an FEM Simulation Driving Support Vector Machine [O] . Xiaoyang Liu, Haizhou Huang, Jiawei Xiang 2020

机译：基于加速度传感器和有限元模拟驱动支持向量机的轴承故障个性化诊断方法
7. A Novel Faults Diagnosis Method for Rolling Element Bearings Based on ELCD and Extreme Learning Machine [O] . Mingliang Liang, Dongmin Su, Daidi Hu, 2018

机译：基于ELCD和极端学习机的滚动元件轴承的新型故障诊断方法

Study on Fault Diagnosis for Bearing Based on VMD-SVD and Extreme Learning Machine

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