Intelligent fault diagnosis of rotating machinery using lightweight network with modified tree‐structured parzen estimators-Reference-Cited by-同舟云学术

Intelligent fault diagnosis of rotating machinery using lightweight network with modified tree‐structured parzen estimators

Published:2022-09 Issue:3 Volume:4 Page:194-207
ISSN:2516-8398
Container-title:IET Collaborative Intelligent Manufacturing
language:en
Short-container-title:IET Collab Intel Manufact

Author:

Liang Jingkang¹^ORCID,Liao Yixiao¹^ORCID,Chen Zhuyun¹²^ORCID,Lin Huibin¹,Jin Gang¹³^ORCID,Gryllias Konstantinos⁴⁵^ORCID,Li Weihua⁶^ORCID

Affiliation:

1. School of Mechanical & Automotive Engineering South China University of Technology Guangzhou China

2. Beijing Key Laboratory of Measurement Control of Mechanical and Electrical System Technology Beijing Information Science Technology University Beijing China

3. Guangdong Provincial Key Laboratory of Technique and Equipment for Macromolecular Advanced Manufacturing Guangzhou China

4. Department of Mechanical Engineering KU Leuven Leuven Belgium

5. Dynamics of Mechanical and Mechatronic Systems Flanders Make Lommel Belgium

6. Shine‐Ming Wu School of Intelligent Engineering South China University of Technology Guangzhou China

Funder

National Natural Science Foundation of China

Publisher

Institution of Engineering and Technology (IET)

Subject

Artificial Intelligence,Industrial and Manufacturing Engineering,Computer Science Applications,Hardware and Architecture

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/cim2.12055

Reference51 articles.

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5. Deep‐learning‐based guided wave detection for liquid‐level state in porcelain bushing type terminal

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