Deep reinforcement learning for class imbalance fault diagnosis of equipment in nuclear power plants

Author:

Zhong XianpingORCID,Zhang LinORCID,Ban HengORCID

Funder

University of Pittsburgh

U.S. Department of Energy

Publisher

Elsevier BV

Subject

Nuclear Energy and Engineering

Reference23 articles.

1. Albawi, S., Mohammed, T.A., Al-Zawi, S., 2018. Understanding of a convolutional neural network. Proc. 2017 Int. Conf. Eng. Technol. ICET 2017 2018-January, 1–6. 10.1109/ICENGTECHNOL.2017.8308186.

2. Baumgarten, S., Brecht, B., Bruhns, U., Fehring, P., 2010. Reactor coolant pump type RUV for Westinghouse reactor AP1000, in: Proceedings of the International Congress on Advances in Nuclear Power Plants, San Diego, CA, June. pp. 12–17.

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. SMOTE: synthetic minority over-sampling technique;Chawla;J. Artif. Intell. Res.,2002

5. C4. 5, class imbalance, and cost sensitivity: why under-sampling beats over-sampling;Drummond;Workshop on Learning from Imbalanced Datasets,2003

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