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

2022 ◽  
Vol 168 ◽  
pp. 108909
Author(s):  
Xianping Zhong ◽  
Heng Ban
Keyword(s):  
Fault Diagnosis ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Ensemble Learning ◽  
Nuclear Power ◽  
Rotating Machine

scholarly journals Fuzzy logic based fault diagnosis of a PWR nuclear power plant

2009 ◽  
Vol 3 (3) ◽  
pp. 296 ◽  
Author(s):  
Roozbeh Razavi Far ◽  
Hadi Davilu ◽  
Caro Lucas
Keyword(s):  
Fuzzy Logic ◽  
Fault Diagnosis ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Nuclear Power

scholarly journals A generic task approach to a real time nuclear power plant fault diagnosis and advisory system

2003 ◽  
Author(s):  
B.K. Hajek ◽  
D.W. Miller ◽  
R. Bhatnagar ◽  
J.E. Stasenko ◽  
W.F. Punch ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Real Time ◽  
Nuclear Power ◽  
Advisory System

scholarly journals Generator stator windings ground fault diagnosis for generator–grid​ directly connected system of floating nuclear power plant

2021 ◽  
Vol 7 ◽  
pp. 460-469
Author(s):  
Yikai Wang ◽  
Xianggen Yin ◽  
Jian Qiao ◽  
Liming Tan ◽  
Wen Xu ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Nuclear Power ◽  
Stator Windings ◽  
Ground Fault ◽  
Generator Stator

scholarly journals The Research of Fault Diagnosis of Nuclear Power Plant Based on ELM-AdaBoost.SAMME

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Cheng Li ◽  
Ren Yu ◽  
Tianshu Wang
Keyword(s):  
Fault Diagnosis ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Bp Neural Network ◽  
Nuclear Power ◽  
Combined Method ◽  
Weak Classifier ◽  
Simulation Experiments ◽  
Learning Machine ◽  
Time Diagnosis

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.

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