Small Break Loss of Coolant Accident (SB-LOCA) fault diagnosis using Adaptive Neuro-Fuzzy Inference System (ANFIS)
| Autor: | Adede Simon Ochieng, Liu Yong-Kuo, Mwaura Anselim Mwangi |
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| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | IOP Conference Series: Earth and Environmental Science. 675:012034 |
| ISSN: | 1755-1315 1755-1307 |
| DOI: | 10.1088/1755-1315/675/1/012034 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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