AMCW-DFFNSA: An interpretable deep feature fusion network for noise-robust machinery fault diagnosis-Reference-Cited by-同舟云学术

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AMCW-DFFNSA: An interpretable deep feature fusion network for noise-robust machinery fault diagnosis

Published:2024-10 Issue: Volume:301 Page:112361
ISSN:0950-7051
Container-title:Knowledge-Based Systems
language:en
Short-container-title:Knowledge-Based Systems

Author:

Han Yan,Lv Sipeng^ORCID,Huang Qingqing^ORCID,Zhang Yan

Publisher

Elsevier BV

Reference37 articles.

1. Global contextual residual convolutional neural networks for motor fault diagnosis under variable-speed conditions;Xu;Reliab. Eng. Syst. Saf.,2022

2. Interpretable physics-informed domain adaptation paradigm for cross-machine transfer diagnosis;He;Knowledge-Based Syst.,2024

3. Difference mode decomposition for adaptive signal decomposition;Hou;Mech. Syst. Signal Proc.,2023

4. Attention guided multi-wavelet adversarial network for cross domain fault diagnosis;Wang;Knowledge-Based Syst.,2024

5. Towards trustworthy machine fault diagnosis: a probabilistic Bayesian deep learning framework;Zhou;Reliab. Eng. Syst. Saf.,2022

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