Fatigue condition diagnosis of rolling bearing based on normalized balanced multiscale sample entropy-Reference-Cited by-同舟云学术

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Fatigue condition diagnosis of rolling bearing based on normalized balanced multiscale sample entropy

Published:2023-07 Issue: Volume:172 Page:107642
ISSN:0142-1123
Container-title:International Journal of Fatigue
language:en
Short-container-title:International Journal of Fatigue

Author:

Tan Hongchuang,Xie Suchao,Liu Runda,Cheng Jiaqi,Jing Kunkun

Funder

National Key Research and Development Program of China

Fundamental Research Funds for Central Universities of the Central South University

National Natural Science Foundation of China

Central South University

Publisher

Elsevier BV

Subject

Industrial and Manufacturing Engineering,Mechanical Engineering,Mechanics of Materials,General Materials Science,Modeling and Simulation

Reference46 articles.

1. Bearing remaining useful life prediction of fatigue degradation process based on dynamic feature construction;Zhu;Int J Fatigue,2022

2. A feature extraction and machine learning framework for bearing fault diagnosis;Cui;Renew Energy,2022

3. Identification and prediction of fatigue crack growth under different stress ratios using acoustic emission data;Chai;Int J Fatigue,2022

4. Compound faults diagnosis and analysis for a wind turbine gearbox via a novel vibration model and empirical wavelet transform;Teng;Renew Energy,2019

5. High-fidelity noise-reconstructed empirical mode decomposition for mechanical multiple and weak fault extractions;Yuan;ISA Trans,2022

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3. Uncertainty‐aware nuclear power turbine vibration fault diagnosis method integrating machine learning and heuristic algorithm;IET Collaborative Intelligent Manufacturing;2024-06-07

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