Remaining useful life prediction via a deep adaptive transformer framework enhanced by graph attention network-Reference-Cited by-同舟云学术

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Remaining useful life prediction via a deep adaptive transformer framework enhanced by graph attention network

Published:2023-09 Issue: Volume:174 Page:107722
ISSN:0142-1123
Container-title:International Journal of Fatigue
language:en
Short-container-title:International Journal of Fatigue

Author:

Liang Pengfei,Li Ying,Wang Bin,Yuan Xiaoming,Zhang Lijie

Funder

Natural Science Foundation of Hebei Province

Publisher

Elsevier BV

Subject

Industrial and Manufacturing Engineering,Mechanical Engineering,Mechanics of Materials,General Materials Science,Modeling and Simulation

Reference44 articles.

1. A novel gear fatigue monitoring indicator and its application to remaining useful life prediction for spur gear in intelligent manufacturing systems;Feng;Int J Fatigue,2023

2. Deep learning for machine health prognostics using Kernel-based feature transformation;Pillai;J Intell Manuf,2022

3. Bearing remaining useful life prediction of fatigue degradation process based on dynamic feature construction;Zhu;Int J Fatigue,2022

4. A Critical Analysis of Crack Propagation Laws;Paris;J Basic Eng,1963

5. Life Prediction for Turbopropulsion Systems Under Dwell Fatigue Conditions;Chan;J Eng Gas Turbines Power,2012

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