Few-shot fault diagnosis of rolling bearing under variable working conditions based on ensemble meta-learning

Che, Changchang; Wang, Huawei; Xiong, Minglan; Ni, Xiaomei

doi:10.1016/j.dsp.2022.103777

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Few-shot fault diagnosis of rolling bearing under variable working conditions based on ensemble meta-learning

Author(s): Changchang Che , Huawei Wang , Minglan Xiong , Xiaomei Ni

Publication date Created: November 2022

Publication date (Print): November 2022

Journal: Digital Signal Processing

Publisher: Elsevier BV

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Fault diagnosis of rolling bearing of wind turbines based on the Variational Mode Decomposition and Deep Convolutional Neural Networks

Zifei Xu, Chun Li, Yang Yang (2020)

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Stacked pruning sparse denoising autoencoder based intelligent fault diagnosis of rolling bearings

Haiping Zhu, Jiaxin Cheng, Cong Zhang … (2020)

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Data synthesis using deep feature enhanced generative adversarial networks for rolling bearing imbalanced fault diagnosis

Shaowei Liu, Hongkai Jiang, Zhenghong Wu … (2022)

0 comments Cited 18 times – based on 0 reviews      Review now

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Journal

Title: Digital Signal Processing

Abbreviated Title: Digital Signal Processing

Publisher: Elsevier BV

ISSN (Print): 10512004

Publication date Created: November 2022

Publication date (Print): November 2022

Volume: 131

Page: 103777

Article

DOI: 10.1016/j.dsp.2022.103777

SO-VID: 5553ea07-2e9b-4cf0-af6f-f74d47e418b1

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Few-shot fault diagnosis of rolling bearing under variable working conditions based on ensemble meta-learning

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Most cited references 33

Fault diagnosis of rolling bearing of wind turbines based on the Variational Mode Decomposition and Deep Convolutional Neural Networks

Stacked pruning sparse denoising autoencoder based intelligent fault diagnosis of rolling bearings

Data synthesis using deep feature enhanced generative adversarial networks for rolling bearing imbalanced fault diagnosis

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