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• W3135499714 startingPage "012034" @default.
• W3135499714 abstract "Abstract The detection of incipient faults of the current fault diagnosis systems in Nuclear Power Plants is inherently limited. Active research in machine learning algorithms like Adaptive Neuro-Fuzzy Inference System (ANFIS) is providing promising results in the prediction of faults. This paper explored four different configurations of Adaptive Neuro-Fuzzy Inference System (ANFIS) methodology in a bid to come up with a superior model that not only had a high sensitivity in the detection of incipient faults but also had superior prediction capabilities. The data-driven ANFIS schemes were used to predict a sensitive fault signature and to evaluate the models, Small Break Loss of Coolant Accident (SBLOCA) transient events were modeled in Qinshan I Nuclear Power Plant. Coefficient of determination, normal probability plot of residuals and mean absolute percent error were used to assess the competencies of the estimation of the models." @default.
• W3135499714 created "2021-03-15" @default.
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• W3135499714 date "2021-02-01" @default.
• W3135499714 modified "2023-09-25" @default.
• W3135499714 title "Small Break Loss of Coolant Accident (SB-LOCA) fault diagnosis using Adaptive Neuro-Fuzzy Inference System (ANFIS)" @default.
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• W3135499714 doi "https://doi.org/10.1088/1755-1315/675/1/012034" @default.
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