Data augmentation via variational mode reconstruction and its application in few-shot fault diagnosis of rolling bearings
Author:
Funder
National Natural Science Foundation of China
Publisher
Elsevier BV
Subject
Applied Mathematics,Electrical and Electronic Engineering,Condensed Matter Physics,Instrumentation
Reference35 articles.
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4. A multi-stage semi-supervised learning approach for intelligent fault diagnosis of rolling bearing using data augmentation and metric learning;Yu;Mech. Syst. Sig. Process.,2021
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1. Failure Mode Classification for Rolling Element Bearings Using Time-Domain Transformer-Based Encoder;Sensors;2024-06-18
2. A Multi-task Learning Method for Few-Shot Fault Diagnosis Based on Metric Learning;Mechanisms and Machine Science;2024
3. Deep residual shrinkage networks with adaptively convex global parametric rectifier linear units for fault diagnosis;Measurement Science and Technology;2023-11-15
4. EC-WGAN: Enhanced Conditional and Wasserstein GAN for Fault Samples Augmentation;2023 6th International Conference on Robotics, Control and Automation Engineering (RCAE);2023-11-03
5. LGMA-DRSN: a lightweight convex global multi-attention deep residual shrinkage network for fault diagnosis;Measurement Science and Technology;2023-08-02
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