Autoregressive Minimum Entropy Deconvolution Enhanced Resonance Features for Fault Diagnosis of Wind Turbine Gearbox-Reference-Cited by-同舟云学术

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Autoregressive Minimum Entropy Deconvolution Enhanced Resonance Features for Fault Diagnosis of Wind Turbine Gearbox

Published:2023-10-20 Issue: Volume: Page:56-61
ISSN:
Container-title:2023 International Conference on Automation, Control and Electronics Engineering (CACEE)
language:
Short-container-title:

Author:

Shi Xiaoyu¹,Sun Kang²

Affiliation:

1. Henan Polytechnic University,School of Electrical Engineering and Automation,Jiaozuo,China

2. Henan Polytechnic University Dianrong Intelligent Technology Co., Ltd.,School of Electrical Engineering and Automation, Henan Key Laboratory of Intelligent Detection and Control of Coal Mine Equipment,Jiaozuo,China

Funder

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10457285/10457293/10457368.pdf?arnumber=10457368

Reference11 articles.

1. Development of wind power industry in China: A comprehensive assessment

2. Fault diagnosis of wind turbine generator bearings using fast spectral correlation

3. Fault diagnosis method of rolling bearings based on adaptive modified CEEMD and 1DCNN model

4. Intelligent Diagnosis of Rotating Machinery Based on Optimized Adaptive Learning Dictionary and 1DCNN

5. Weak fault feature extraction of rolling bearings based on improved ensemble noise-reconstructed EMD and adaptive threshold denoising

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