Data-driven bearing health management using a novel multi-scale fused feature and gated recurrent unit-Reference-Cited by-同舟云学术

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Data-driven bearing health management using a novel multi-scale fused feature and gated recurrent unit

Published:2024-02 Issue: Volume:242 Page:109753
ISSN:0951-8320
Container-title:Reliability Engineering & System Safety
language:en
Short-container-title:Reliability Engineering & System Safety

Author:

Ni Qing,Ji J.C.,Feng Ke,Zhang Yongchao,Lin Dongdong,Zheng Jinde

Funder

Australian Research Council

Publisher

Elsevier BV

Subject

Industrial and Manufacturing Engineering,Safety, Risk, Reliability and Quality

Reference48 articles.

1. Digital twin-driven partial domain adaptation network for intelligent fault diagnosis of rolling bearing;Zhang;Reliab Eng Syst Saf,2023

2. An adversarial transfer network with supervised metric for remaining useful life prediction of rolling bearing under multiple working conditions;Zhuang;Reliab Eng Syst Saf,2022

3. A survey of condition monitoring and protection methods for medium-voltage induction motors;Zhang;IEEE Trans Ind Appl,2010

4. Attention-based multiscale denoising residual convolutional neural networks for fault diagnosis of rotating machinery;Xu;Reliab Eng Syst Saf,2022

5. Data-driven prognostic scheme for bearings based on a novel health indicator and gated recurrent unit network;Ni;IEEE Trans Ind Inf,2023

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