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The Research of Fault Diagnosis of Nuclear Power Plant Based on ELM-AdaBoost.SAMME
Joint Authors
Source
Science and Technology of Nuclear Installations
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-12-21
Country of Publication
Egypt
No. of Pages
9
Abstract EN
A fault diagnosis framework based on extreme learning machine (ELM) and AdaBoost.SAMME is proposed in a nuclear power plant (NPP) in this paper.
After briefly describing the principles of ELM and AdaBoost.SAMME algorithm, the fault diagnosis framework sets ELM algorithm as the weak classifier and then integrates several weak classifiers into a strong one using the AdaBoost.SAMME algorithm.
Furthermore, some experiments are put forward for the setting of two algorithms.
The results of simulation experiments on the HPR1000 simulator show that the combined method has higher precision and faster speed by improving the performance of weak classifiers compared to the BP neural network and verify the feasibility and validity of the ensemble learning method for fault diagnosis.
Meanwhile, the results also indicate that the proposed method can meet the requirements of a real-time diagnosis of the nuclear power plant.
American Psychological Association (APA)
Li, Cheng& Yu, Ren& Wang, Tianshu. 2020. The Research of Fault Diagnosis of Nuclear Power Plant Based on ELM-AdaBoost.SAMME. Science and Technology of Nuclear Installations،Vol. 2020, no. 2020, pp.1-9.
https://search.emarefa.net/detail/BIM-1209457
Modern Language Association (MLA)
Li, Cheng…[et al.]. The Research of Fault Diagnosis of Nuclear Power Plant Based on ELM-AdaBoost.SAMME. Science and Technology of Nuclear Installations No. 2020 (2020), pp.1-9.
https://search.emarefa.net/detail/BIM-1209457
American Medical Association (AMA)
Li, Cheng& Yu, Ren& Wang, Tianshu. The Research of Fault Diagnosis of Nuclear Power Plant Based on ELM-AdaBoost.SAMME. Science and Technology of Nuclear Installations. 2020. Vol. 2020, no. 2020, pp.1-9.
https://search.emarefa.net/detail/BIM-1209457
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1209457