Data-driven predictive maintenance strategy considering the uncertainty in remaining useful life prediction-Reference-Cited by-同舟云学术

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Data-driven predictive maintenance strategy considering the uncertainty in remaining useful life prediction

Published:2022-07 Issue: Volume:494 Page:79-88
ISSN:0925-2312
Container-title:Neurocomputing
language:en
Short-container-title:Neurocomputing

Author:

Chen Chuang,Shi Jiantao,Lu Ningyun,Zhu Zheng Hong,Jiang Bin

Funder

Natural Science Foundation of Jiangsu Province

Aeronautical Science Foundation of China

National Natural Science Foundation of China

China Scholarship Council

Nanjing University of Aeronautics and Astronautics

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference49 articles.

1. Data-driven based fault prognosis for industrial systems: A concise overview;Zhong;IEEE/CAA J. Autom. Sinica,2020

2. A rolling-horizon approach for predictive maintenance planning to reduce the risk of rail service disruptions;Consilvio;IEEE Trans. Reliab.,2021

3. Condition-based maintenance optimization for continuously monitored degrading systems under imperfect maintenance actions;Chen;J. Syst. Eng. Electron.,2020

4. A data-driven degradation prognostic strategy for aero-engine under various operational conditions;Wang;Neurocomputing,2021

5. A new hybrid fault prognosis method for MFS systems based on distributed neural networks and recursive bayesian algorithm;Kordestani;IEEE Syst. J.,2020

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