Research on an Improved Wasserstein Generative Adversarial Network Early Fault Warning Method for Rotating Machinery-Reference-Cited by-同舟云学术

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Research on an Improved Wasserstein Generative Adversarial Network Early Fault Warning Method for Rotating Machinery

Published:2024 Issue: Volume:12 Page:109109-109127
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Zhou Chunlei¹^ORCID,Xiao Wang²,Wang Qingfeng¹^ORCID,Feng Zhipeng³^ORCID

Affiliation:

1. State Key Laboratory of High-End Compressor and System Technology, Beijing University of Chemical Technology, Beijing, China

2. Western Branch of National Pipe Network Group United Pipeline Company Ltd., Ürümqi, China

3. School of Mechanical Engineering, University of Science and Technology Beijing, Beijing, China

Funder

National Key Research and Development Program of China

Major Projects of PipeChina

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/6287639/10380310/10623426.pdf?arnumber=10623426

Reference33 articles.

1. Diagnosis of Incipient Fault Based on Sliding-Scale Resampling Strategy and Improved Deep Autoencoder

2. Rolling bearing fault early warning method based on beta distribution and filtering noise reduction algorithm;Tian;Chin. J. Sci. Instrum.,2023

3. Cycle kurtosis entropy guided symplectic geometry mode decomposition for detecting faults in rotating machinery

4. Initial fault diagnosis of bearing based on AVMD-SE and multiscale enhanced morphological top-hat filter

5. A Novel Empirical Variational Mode Decomposition for Early Fault Feature Extraction

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