MRCFN: A multi-sensor residual convolutional fusion network for intelligent fault diagnosis of bearings in noisy and small sample scenarios-Reference-Cited by-同舟云学术

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MRCFN: A multi-sensor residual convolutional fusion network for intelligent fault diagnosis of bearings in noisy and small sample scenarios

Published:2025-01 Issue: Volume:259 Page:125214
ISSN:0957-4174
Container-title:Expert Systems with Applications
language:en
Short-container-title:Expert Systems with Applications

Author:

Ye Maoyou,Yan Xiaoan^ORCID,Hua Xing,Jiang Dong^ORCID,Xiang Ling^ORCID,Chen Ning

Publisher

Elsevier BV

Reference56 articles.

1. Fractional Fourier and time domain recurrence plot fusion combining convolutional neural network for bearing fault diagnosis under variable working conditions;Bai;Reliability Engineering & System Safety,2023

2. Improving augmentation consistency for graph contrastive learning;Bu;Pattern Recognition,2024

3. Rotating rectifier fault diagnosis of nuclear multiphase brushless excitation system based on DTW metric and KNN classifier;Cai;IEEE Transactions on Power Electronics,2023

4. A novel classification method based on ICGOA-KELM for fault diagnosis of rolling bearing;Chen;Applied Intelligence,2020

5. M2FN: An end-to-end multi-task and multi-sensor fusion network for intelligent fault diagnosis;Cui;Measurement,2022

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