Hybrid distance-guided adversarial network for intelligent fault diagnosis under different working conditions-Reference-Cited by-同舟云学术

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Hybrid distance-guided adversarial network for intelligent fault diagnosis under different working conditions

Published:2021-05 Issue: Volume:176 Page:109197
ISSN:0263-2241
Container-title:Measurement
language:en
Short-container-title:Measurement

Author:

Han Baokun,Zhang Xiao,Wang Jinrui,An Zenghui,Jia Sixiang,Zhang Guowei

Funder

China Postdoctoral Science Foundation

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

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

Reference41 articles.

1. Deep learning;LeCun;Nature,2015

2. Deep representation clustering-based fault diagnosis method with unsupervised data applied to rotating machinery;Li;Mech. Syst. Sig. Process.,2020

3. A novel intelligent fault diagnosis method based on fast intrinsic component filtering and pseudo-normalization;Zhang;Mech. Syst. Sig. Process.,2020

4. Batch-normalized deep neural networks for achieving fast intelligent fault diagnosis of machines;Wang;Neurocomputing,2019

5. (k, n) scalable secret image sharing with multiple decoding options;Liu;J. Intell. Fuzzy Syst.,2019

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