Prediction of the Loss of Feed Water Fault Signatures Using Machine Learning Techniques
Fault diagnosis occurrence and its precise prediction in nuclear power plants are extremely important in avoiding disastrous consequences. The inherent limitations of the current fault diagnosis methods make machine learning techniques and their hybrid methodologies possible solutions to remedy this...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Science and Technology of Nuclear Installations |
| Online Access: | http://dx.doi.org/10.1155/2021/5511735 |
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| author | Anselim M. Mwaura Yong-Kuo Liu |
| author_facet | Anselim M. Mwaura Yong-Kuo Liu |
| author_sort | Anselim M. Mwaura |
| collection | DOAJ |
| description | Fault diagnosis occurrence and its precise prediction in nuclear power plants are extremely important in avoiding disastrous consequences. The inherent limitations of the current fault diagnosis methods make machine learning techniques and their hybrid methodologies possible solutions to remedy this challenge. This study sought to develop, examine, compare, and contrast three robust machine learning methodologies of adaptive neurofuzzy inference system, long short-term memory, and radial basis function network by modeling the loss of feed water event using RELAP5. The performance indices of residual plots, mean absolute percentage error, root mean squared error, and coefficient of determination were used to determine the most suitable algorithms for accurately diagnosing the loss of feed water transient signatures. The study found out that the adaptive neurofuzzy inference system model outperformed the other schemes when predicting the temperature of the steam generator tubes, the radial basis function network scheme was best suited in forecasting the mass flow rate at the core inlet, while the long short-term memory algorithm was best suited for the estimation of the severities of the loss of the feed water fault. |
| format | Article |
| id | doaj-art-6bf1ff53a5004266becafa90186ccd88 |
| institution | Kabale University |
| issn | 1687-6075 1687-6083 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Science and Technology of Nuclear Installations |
| spelling | doaj-art-6bf1ff53a5004266becafa90186ccd882025-02-03T00:59:06ZengWileyScience and Technology of Nuclear Installations1687-60751687-60832021-01-01202110.1155/2021/55117355511735Prediction of the Loss of Feed Water Fault Signatures Using Machine Learning TechniquesAnselim M. Mwaura0Yong-Kuo Liu1Fundamental Science on Nuclear Safety and Simulation Technology Laboratory, Harbin Engineering University, No. 145, Harbin 150001, ChinaFundamental Science on Nuclear Safety and Simulation Technology Laboratory, Harbin Engineering University, No. 145, Harbin 150001, ChinaFault diagnosis occurrence and its precise prediction in nuclear power plants are extremely important in avoiding disastrous consequences. The inherent limitations of the current fault diagnosis methods make machine learning techniques and their hybrid methodologies possible solutions to remedy this challenge. This study sought to develop, examine, compare, and contrast three robust machine learning methodologies of adaptive neurofuzzy inference system, long short-term memory, and radial basis function network by modeling the loss of feed water event using RELAP5. The performance indices of residual plots, mean absolute percentage error, root mean squared error, and coefficient of determination were used to determine the most suitable algorithms for accurately diagnosing the loss of feed water transient signatures. The study found out that the adaptive neurofuzzy inference system model outperformed the other schemes when predicting the temperature of the steam generator tubes, the radial basis function network scheme was best suited in forecasting the mass flow rate at the core inlet, while the long short-term memory algorithm was best suited for the estimation of the severities of the loss of the feed water fault.http://dx.doi.org/10.1155/2021/5511735 |
| spellingShingle | Anselim M. Mwaura Yong-Kuo Liu Prediction of the Loss of Feed Water Fault Signatures Using Machine Learning Techniques Science and Technology of Nuclear Installations |
| title | Prediction of the Loss of Feed Water Fault Signatures Using Machine Learning Techniques |
| title_full | Prediction of the Loss of Feed Water Fault Signatures Using Machine Learning Techniques |
| title_fullStr | Prediction of the Loss of Feed Water Fault Signatures Using Machine Learning Techniques |
| title_full_unstemmed | Prediction of the Loss of Feed Water Fault Signatures Using Machine Learning Techniques |
| title_short | Prediction of the Loss of Feed Water Fault Signatures Using Machine Learning Techniques |
| title_sort | prediction of the loss of feed water fault signatures using machine learning techniques |
| url | http://dx.doi.org/10.1155/2021/5511735 |
| work_keys_str_mv | AT anselimmmwaura predictionofthelossoffeedwaterfaultsignaturesusingmachinelearningtechniques AT yongkuoliu predictionofthelossoffeedwaterfaultsignaturesusingmachinelearningtechniques |