Deep‐learning‐based nuclear power plant fault detection using remote light‐emitting diode array data transmission

2021 ◽  
Author(s):  
Yourak Choi ◽  
Ji‐Hoon Bae ◽  
Doyeob Yeo ◽  
Dongyun Cho ◽  
Jaecheol Lee ◽  
...  
Keyword(s):  
Deep Learning ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Fault Detection ◽  
Data Transmission ◽  
Nuclear Power ◽  
Light Emitting Diode ◽  
Diode Array ◽  
Array Data ◽  
Light Emitting

Data-Driven Fault Diagnosis for Nuclear Power Plant: The Implicit Model Approach

2018 ◽  
Author(s):  
Zhaoxu Chen ◽  
Xianling Li ◽  
Zhiwu Ke ◽  
Mo Tao ◽  
Yi Feng
Keyword(s):  
Power Plant ◽  
Nuclear Power Plant ◽  
Fault Detection ◽  
Nuclear Power ◽  
Model Identification ◽  
Industrial Process ◽  
Data Driven ◽  
Implicit Model ◽  
Complicated Model ◽  
Model Approach

This paper proposes a data-driven fault detection approach for nuclear power plant. The approach starts from input and output (I/O) data obtained from operating data of industrial process. Due to the model is not explicitly appeared, the proposed approach is named as implicit model approach (IMA). Residual generator is obtained directly from I/O data rather than from the mechanism, based which the algorithm of IMA-based fault detection is proposed. The main advantage of IMA-based fault detection is that it can circumvent complicated model identification. The approach generates parameterized matrices of residual signal inspired by subspace relevant technology without any prior knowledge about mechanisms of the plant. Fault information has been injected to a simulating platform of a compact reactor in the simulation part, by which we verify the effectiveness of IMA-based fault detection.

Application of Possibilistic C-Means for Fault Detection in Nuclear Power Plant Data

2015 ◽  
Vol 137 (6) ◽  
Author(s):  
Annalisa Perasso ◽  
Cristina Campi ◽  
Cristian Toraci ◽  
Francesco Benvenuto ◽  
Michele Piana ◽  
...  
Keyword(s):  
Time Series ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Fault Detection ◽  
Nuclear Power ◽  
Clustering Algorithm ◽  
Principal Component ◽  
Signal Classification ◽  
Classification Method ◽  
Plant Data

This paper describes a classification method for automatic fault detection in nuclear power plant (NPP) data. The method takes as input time series associated to specific parameters and realizes signal classification by using a clustering algorithm based on possibilistic C-means (PCM). This approach is applied to time series recorded in a CANDU® power plant and is validated by comparison with results provided by a classification method based on principal component analysis (PCA).

Nuclear power plant sensor signal reconstruction based on deep learning methods

2022 ◽  
Vol 167 ◽  
pp. 108765
Author(s):  
Zixiao Yang ◽  
Peng Xu ◽  
Biao Zhang ◽  
Chuanlong Xu ◽  
Liming Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Nuclear Power ◽  
Signal Reconstruction ◽  
Sensor Signal ◽  
Learning Methods
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