A wavelet packet transform-based deep feature transfer learning method for bearing fault diagnosis under different working conditions-Reference-Cited by-同舟云学术

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A wavelet packet transform-based deep feature transfer learning method for bearing fault diagnosis under different working conditions

Published:2022-09 Issue: Volume:201 Page:111597
ISSN:0263-2241
Container-title:Measurement
language:en
Short-container-title:Measurement

Author:

Yu Xiao,Liang Zhongting,Wang Youjie,Yin Hongshen,Liu Xiaowen^ORCID,Yu Wanli,Huang Yanqiu^ORCID

Funder

National Natural Science Foundation of China

China University of Mining and Technology

Key Technologies Research and Development Program

Publisher

Elsevier BV

Subject

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

Reference43 articles.

1. A novel weighted adversarial transfer network for partial domain fault diagnosis of machinery;Li;IEEE Trans. Ind. Inf.,2020

2. A new multiple source domain adaptation fault diagnosis method between different rotating machines;Zhu;IEEE Trans. Ind. Inf.,2020

3. An intelligent fault diagnosis approach based on transfer learning from laboratory bearings to locomotive bearings;Yang;Mech. Syst. Signal Process.,2019

4. Feature denoising and nearest-farthest distance preserving projection for machine fault diagnosis;Li;IEEE Trans. Ind. Inf.,2015

5. Quantitative and localization diagnosis of a defective ball bearing based on vertical-horizontal synchronization signal analysis;Cui;IEEE Trans. Ind. Electron.,2017

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