Digital twin-assisted interpretable transfer learning: A novel wavelet-based framework for intelligent fault diagnostics from simulated domain to real industrial domain-Reference-Cited by-同舟云学术

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Digital twin-assisted interpretable transfer learning: A novel wavelet-based framework for intelligent fault diagnostics from simulated domain to real industrial domain

Published:2024-10 Issue: Volume:62 Page:102681
ISSN:1474-0346
Container-title:Advanced Engineering Informatics
language:en
Short-container-title:Advanced Engineering Informatics

Author:

Li Sheng^ORCID,Jiang Qiubo,Xu Yadong^ORCID,Feng Ke,Zhao Zhiheng,Sun Beibei,Huang George Q.

Publisher

Elsevier BV

Reference56 articles.

1. Dconformer: A denoising convolutional transformer with joint learning strategy for intelligent diagnosis of bearing faults;Li;Mech. Syst. Sig. Process.,2024

2. Entropy based fault classification using the case western reserve university data: a benchmark study;Li;IEEE Trans. Rel.,2020

3. Online fault diagnosis method based on transfer convolutional neural networks;Xu;IEEE Trans. Instrum. Meas.,2020

4. Gearbox fault diagnosis using a deep learning model with limited data sample;Saufi;IEEE Trans. Ind. Inf.,2020

5. A hybrid deep-learning model for fault diagnosis of rolling bearings;Xu;Measurement,2021

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