Early fault feature extraction of bearings based on Teager energy operator and optimal VMD-Reference-Cited by-同舟云学术

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Early fault feature extraction of bearings based on Teager energy operator and optimal VMD

Published:2019-03 Issue: Volume:86 Page:249-265
ISSN:0019-0578
Container-title:ISA Transactions
language:en
Short-container-title:ISA Transactions

Author:

Xu Bo,Zhou Fengxing,Li Huipeng,Yan Baokang,Liu Yi

Funder

National Natural Science Foundation of China

Research Project of Hubei Provincial Department of Education, China

Hubei Provincial Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Applied Mathematics,Control and Systems Engineering,Electrical and Electronic Engineering,Computer Science Applications,Instrumentation

Reference41 articles.

1. Deep learning based approach for bearing fault diagnosis;He;IEEE Trans Ind Appl,2017

2. Fuzzy diagnosis method for rotating machinery in variable rotating speed;Wang;IEEE Sens J,2011

3. Online unbalanced rotor fault detection of an IMD rive based on both time and frequency domain analyses;Mizanur Rahman;IEEE Trans Ind Appl,2017

4. A new approach based on oma-empirical wavelet transforms for bearing fault diagnosis;Mourad;Measurement,2016

5. Application of emd-wvd and particle filter for gearbox fault feature extraction and remaining useful life prediction;Liu;J Vibroeng,2017

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1. A Novel Method Based on Stepwise Variational Modal Decomposition and Gramian Angular Difference Field for Bearing Health Monitoring;Arabian Journal for Science and Engineering;2024-08-05

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3. Feature extraction based on vibration signal decomposition for fault diagnosis of rolling bearings;The International Journal of Advanced Manufacturing Technology;2023-12-02

4. Adaptive range selection for parameter optimization of VMD algorithm in rolling bearing fault diagnosis under strong background noise;Journal of Mechanical Science and Technology;2023-11

5. Review of spectrum analysis in fault diagnosis for mechanical equipment;Engineering Research Express;2023-10-11

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