An adversarial transfer network with supervised metric for remaining useful life prediction of rolling bearing under multiple working conditions-Reference-Cited by-同舟云学术

An adversarial transfer network with supervised metric for remaining useful life prediction of rolling bearing under multiple working conditions

Published:2022-09 Issue: Volume:225 Page:108599
ISSN:0951-8320
Container-title:Reliability Engineering & System Safety
language:en
Short-container-title:Reliability Engineering & System Safety

Author:

Zhuang Jichao^ORCID,Jia Minping,Zhao Xiaoli

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

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

Reference34 articles.

1. A bidirectional LSTM prognostics method under multiple operational conditions;Huang;IEEE Trans Ind Electron,2019

2. Prediction of remaining useful life based on bidirectional gated recurrent unit with temporal self-attention mechanism;Zhang;Reliab Eng Syst Saf,2022

3. Highly efficient fault diagnosis of rotating machinery under time-varying speeds using LSISMM and small infrared thermal images;Li;IEEE Trans Syst Man Cybern Syst,2022

4. Temporal convolution-based transferable cross-domain adaptation approach for remaining useful life estimation under variable failure behaviors;Zhuang;Reliab Eng Syst Saf,2021

5. Multi-bearing remaining useful life collaborative prediction: a deep learning approach;Ren;J Manuf Syst,2017

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