Intelligent machine fault diagnosis with effective denoising using EEMD-ICA- FuzzyEn and CNN-Reference-Cited by-同舟云学术

Intelligent machine fault diagnosis with effective denoising using EEMD-ICA- FuzzyEn and CNN

Published:2022-09-19 Issue: Volume: Page:1-13
ISSN:0020-7543
Container-title:International Journal of Production Research
language:en
Short-container-title:International Journal of Production Research

Author:

Zhou Hanting¹²,Chen Wenhe¹,Shen Changqing³,Cheng Longsheng¹,Xia Min²

Affiliation:

1. School of economics and management, Nanjing University of Science and Technology, Nanjing, People’s Republic of China

2. School of Engineering, Lancaster University, Lancaster, United Kingdom

3. School of Rail Transportation, Soochow University, Suzhou, People’s Republic of China

Funder

China Scholarship Council

Publisher

Informa UK Limited

Subject

Industrial and Manufacturing Engineering,Management Science and Operations Research,Strategy and Management

Link

https://www.tandfonline.com/doi/pdf/10.1080/00207543.2022.2122621

Reference34 articles.

1. Generalised spatially weighted autocorrelation approach for monitoring and diagnosing faults in 3D topographic surfaces

2. Inherent Fuzzy Entropy for the Improvement of EEG Complexity Evaluation

3. “Case Western Reserve University Bearing Data Centre Website.” 2021. In., edited by https://engineering.case.edu/bearingdatacenter/download-data-file

4. An efficient ICA-DW-SVDD fault detection and diagnosis method for non-Gaussian processes

5. Bearing fault diagnosis base on multi-scale CNN and LSTM model

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