Causality-inspired multi-source domain generalization method for intelligent fault diagnosis under unknown operating conditions-Reference-Cited by-同舟云学术

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Causality-inspired multi-source domain generalization method for intelligent fault diagnosis under unknown operating conditions

Published:2024-12 Issue: Volume:252 Page:110439
ISSN:0951-8320
Container-title:Reliability Engineering & System Safety
language:en
Short-container-title:Reliability Engineering & System Safety

Author:

Ma Hongbo,Wei Jiacheng,Zhang Guowei,Kong Xianguang,Du Jingli^ORCID

Publisher

Elsevier BV

Reference44 articles.

1. Fractional Fourier and time domain recurrence plot fusion combining convolutional neural network for bearing fault diagnosis under variable working conditions;Bai;Reliab Eng Syst Saf,2023

2. Autoencoder-based representation learning and its application in intelligent fault diagnosis: a review;Yang;Measurement,2022

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

4. Diagnosing rotating machines with weakly supervised data using deep transfer learning;Li;IEEE Trans Industr Inform,2020

5. Partial transfer learning in machinery cross-domain fault diagnostics using class-weighted adversarial networks;Li;Neural Networks,2020

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