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

2022 ◽  
Vol 168 ◽  
pp. 108909
Xianping Zhong ◽  
Heng Ban
B.K. Hajek ◽  
D.W. Miller ◽  
R. Bhatnagar ◽  
J.E. Stasenko ◽  
W.F. Punch ◽  

2021 ◽  
Vol 7 ◽  
pp. 460-469
Yikai Wang ◽  
Xianggen Yin ◽  
Jian Qiao ◽  
Liming Tan ◽  
Wen Xu ◽  

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Cheng Li ◽  
Ren Yu ◽  
Tianshu Wang

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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