Crack fault diagnosis of rotating machine in nuclear power plant based on ensemble learning-Reference-Cited by-同舟云学术

Crack fault diagnosis of rotating machine in nuclear power plant based on ensemble learning

Published:2022-04 Issue: Volume:168 Page:108909
ISSN:0306-4549
Container-title:Annals of Nuclear Energy
language:en
Short-container-title:Annals of Nuclear Energy

Author:

Zhong Xianping,Ban Heng

Funder

U.S. Department of Energy

Publisher

Elsevier BV

Subject

Nuclear Energy and Engineering

Reference23 articles.

1. Investigation of helix-shaped and transverse crack propagation in rotor shafts based on disk shrunk technology;Andrier;Nucl. Eng. Des.,2006

2. Crack detectability in vertical axis cooling pumps during operation;Bachschmid;Int. J. Rotating Mach.,2004

3. Bearing Data Center | Case School of Engineering | Case Western Reserve University [WWW Document], n.d. URL: https://engineering.case.edu/bearingdatacenter (accessed 10.28.21).

4. Nuclear Reactor Instrumentation (in-core);Boland,1970

5. Random Forests;Breiman;Mach. Learn.,2001

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