A fault diagnosis method for roller bearing based on empirical wavelet transform decomposition with adaptive empirical mode segmentation-Reference-Cited by-同舟云学术

A fault diagnosis method for roller bearing based on empirical wavelet transform decomposition with adaptive empirical mode segmentation

Published:2018-03 Issue: Volume:117 Page:266-276
ISSN:0263-2241
Container-title:Measurement
language:en
Short-container-title:Measurement

Author:

Song Yueheng,Zeng Shengkui^ORCID,Ma Jiming,Guo Jianbin

Publisher

Elsevier BV

Subject

Applied Mathematics,Electrical and Electronic Engineering,Condensed Matter Physics,Instrumentation

Reference30 articles.

1. A review on empirical mode decomposition in fault diagnosis of rotating machinery;Lei;Mech. Syst. Sig. Process.,2013

2. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis;Huang;Proc.: Math. Phys. Eng. Sci.,1998

3. Application of EMD method and Hilbert spectrum to the fault diagnosis of roller bearings;Yu;Mech. Syst. Sig. Process.,2005

4. Machine fault feature extraction based on intrinsic mode functions;Fan;Meas. Sci. Technol.,2008

5. Adaptive parameterless empirical wavelet transform based time-frequency analysis method and its application to rotor rubbing fault diagnosis;Zheng;Signal Process.,2017

Cited by 75 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A rotor bearing system fault diagnosis method based on FSASCA-VMD and GraphSAGE-SA;Measurement Science and Technology;2024-06-28

2. Condition Monitoring and Fault Diagnosis of Rotating Machinery Towards Intelligent Manufacturing: Review and Prospect;Iranian Journal of Science and Technology, Transactions of Mechanical Engineering;2024-06-11

3. Variational Mode Decomposition and Empirical Wavelet Transform-Based Feature Extraction and Ensemble Classifier for Lower Limb Movement Prediction With Surface Electromyography Signal;IEEE Access;2024

4. Multi-condition tool wear prediction for milling CFRP base on a novel hybrid monitoring method;Measurement Science and Technology;2023-12-18

5. Wind power forecasting based on hybrid CEEMDAN-EWT deep learning method;Renewable Energy;2023-12