Rotating machinery fault diagnosis based on convolutional neural network and infrared thermal imaging-Reference-Cited by-同舟云学术

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Rotating machinery fault diagnosis based on convolutional neural network and infrared thermal imaging

Published:2020-02 Issue:2 Volume:33 Page:427-438
ISSN:1000-9361
Container-title:Chinese Journal of Aeronautics
language:en
Short-container-title:Chinese Journal of Aeronautics

Author:

LI Yongbo,DU Xiaoqiang,WAN Fangyi,WANG Xianzhi,YU Huangchao

Funder

National Natural Science Foundation of China

Postdoctoral Innovative Talent Plan, China

Postdoctoral Science Funds, China

Key Research Program, Shaanxi Province

Publisher

Elsevier BV

Subject

Mechanical Engineering,Aerospace Engineering

Reference44 articles.

1. Recent advances in time-frequency analysis methods for machinery fault diagnosis: a review with application examples;Feng;Mech Syst Signal Process,2013

2. Li Y, Wang X, Si S, Huang S. Entropy based fault classification using the case western reserve university data: a benchmark study. IEEE Trans Reliab 2019. (2019-03-07) [2019-03-28]. Available from: https://doi.org/10.1109/TR.2019.2896240.

3. Research and application of improved adaptive momeda fault diagnosis method;Wang;Measurement,2019

4. Application of parameter optimized variational mode decomposition method in fault diagnosis of gearbox;Wang;IEEE Access,2019

5. The entropy algorithm and its variants in the fault diagnosis of rotating machinery: a review;Li;IEEE Access,2018

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