Generative Adversarial Network With Dual Multiscale Feature Fusion for Data Augmentation in Fault Diagnosis-Reference-Cited by-同舟云学术

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Generative Adversarial Network With Dual Multiscale Feature Fusion for Data Augmentation in Fault Diagnosis

Published:2023 Issue: Volume:72 Page:1-17
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Ren Zhijun¹^ORCID,Ji Jinchen²^ORCID,Zhu Yongsheng¹^ORCID,Hong Jun¹^ORCID,Feng Ke³^ORCID

Affiliation:

1. Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, Xi’an Jiaotong University, Xi’an, China

2. School of Mechanical and Mechatronic Engineering, University of Technology Sydney, Ultimo, NSW, Australia

3. School of Engineering, The University of British Columbia, Kelowna, Canada

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/19/10012124/10234438.pdf?arnumber=10234438

Reference55 articles.

1. Enhanced data-driven fault diagnosis for machines with small and unbalanced data based on variational auto-encoder

2. Rolling bearing fault diagnosis using variational autoencoding generative adversarial networks with deep regret analysis

3. A deep ensemble dense convolutional neural network for rolling bearing fault diagnosis

4. Prediction of wind turbine blades icing based on feature Selection and 1D-CNN-SBiGRU

5. Imbalanced Fault Diagnosis of Rolling Bearing Using Enhanced Generative Adversarial Networks

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1. Feature Constrained Encoding Generative Adversarial Prediction Network for Quality Measurement Modeling Under Multitime Scales Data Imbalance In Process Industry;IEEE Sensors Journal;2024-07-01

2. Health prognosis of bearings based on transferable autoregressive recurrent adaptation with few-shot learning;Mechanical Systems and Signal Processing;2024-04

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