Nuclear Power Plant Thermocouple Sensor Fault Detection and Classification using Deep Learning and Generalized Likelihood Ratio Test

2017 ◽  
pp. 1-1 ◽  
Author(s):  
Shyamapada Mandal ◽  
B. Santhi ◽  
S. Sridhar ◽  
K. Vinolia ◽  
P. Swaminathan
Keyword(s):  
Deep Learning ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Fault Detection ◽  
Likelihood Ratio Test ◽  
Nuclear Power ◽  
Generalized Likelihood Ratio Test ◽  
Ratio Test ◽  
Sensor Fault ◽  
Sensor Fault Detection

Sensor fault detection in Nuclear Power Plant using statistical methods

2017 ◽  
Vol 324 ◽  
pp. 103-110 ◽  
Author(s):  
Shyamapada Mandal ◽  
B. Santhi ◽  
S. Sridhar ◽  
K. Vinolia ◽  
P. Swaminathan
Keyword(s):  
Power Plant ◽  
Nuclear Power Plant ◽  
Fault Detection ◽  
Statistical Methods ◽  
Nuclear Power ◽  
Sensor Fault ◽  
Sensor Fault Detection

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.

Kernel Generalized Likelihood Ratio Test for Fault Detection of Biological Systems

2018 ◽  
Vol 17 (4) ◽  
pp. 498-506 ◽  
Author(s):  
Majdi Mansouri ◽  
Raoudha Baklouti ◽  
Mohamed Faouzi Harkat ◽  
Mohamed Nounou ◽  
Hazem Nounou ◽  
...  
Keyword(s):  
Fault Detection ◽  
Likelihood Ratio ◽  
Likelihood Ratio Test ◽  
Biological Systems ◽  
Generalized Likelihood Ratio Test ◽  
Ratio Test ◽  
Generalized Likelihood Ratio
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