A unified framework incorporating predictive generative denoising autoencoder and deep Coral network for rolling bearing fault diagnosis with unbalanced data-Reference-Cited by-同舟云学术

A unified framework incorporating predictive generative denoising autoencoder and deep Coral network for rolling bearing fault diagnosis with unbalanced data

Published:2021-06 Issue: Volume:178 Page:109345
ISSN:0263-2241
Container-title:Measurement
language:en
Short-container-title:Measurement

Author:

Li Xingqiu,Jiang Hongkai,Liu Shaowei,Zhang Jianjun,Xu Jun

Funder

Northwestern Polytechnical University

Aeronautical Science Foundation of China

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Applied Mathematics,Electrical and Electronic Engineering,Condensed Matter Physics,Instrumentation

Reference33 articles.

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3. Rotating machinery fault diagnosis based on convolutional neural network and infrared thermal imaging;Li;Chin. J. Aeronaut.,2020

4. Fault diagnosis method of rolling bearing based on multiple classifier ensemble of the weighted and balanced distribution adaptation under limited sample imbalance;Chen;ISA Trans.,2020

5. DBIG-US: a two-stage under-sampling algorithm to face the class imbalance problem;Guzmán-Ponce;Expert Syst. Appl.,2021

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