DESIGNING AN IMPROVED NEURAL NETWORK FOR THE EARLY DETECTION OF ANOMALIES IN NUCLEAR POWER PLANTS
The effectiveness and dependability of these vital energy infrastructures depend heavily on the early detection of anomalies in nuclear power plants (NPPs). Anomalies in a plant's operations might be signs of the impending equipment failure, a danger to workers' safety, or departure from i...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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University of Kragujevac
2023-08-01
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| Series: | Proceedings on Engineering Sciences |
| Subjects: | |
| Online Access: | https://pesjournal.net/journal/v5-nS1/9.pdf |
| _version_ | 1797691154489147392 |
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| author | Solomon Jebaraj Davendra Kumar Doda Vineet Saxena |
| author_facet | Solomon Jebaraj Davendra Kumar Doda Vineet Saxena |
| author_sort | Solomon Jebaraj |
| collection | DOAJ |
| description | The effectiveness and dependability of these vital energy infrastructures depend heavily on the early detection of anomalies in nuclear power plants (NPPs). Anomalies in a plant's operations might be signs of the impending equipment failure, a danger to workers' safety, or departure from ideal performance, all of which call for quick attention and preventative actions. Traditional NPP monitoring methods depend on the human inspections and predetermined thresholds, which are only sometimes successful in picking up the complicated irregularities. This Study introduces a new, Improved Bat and Grey Wolf Optimized Recurrent Neural Network (IBGWO-RNN) approach to detect the anomalies in NPPs. In this case, the RNN classification effectiveness is increased by using the IBGWO method. The American Nuclear Society ANSI / ANS-3.5 Nuclear Simulator Standard dataset has been used to assess the success of the suggested approach. Each input feature vector will be normalized by using the Z-score Normalization. A Kernel Principal Component Analysis (KPCA) is performed to extract the properties from segmented data. The results of the research show that the recommended methodology beats earlier approaches in terms of the Accuracy, Precision, Recall, and F1-score. Our suggested approach advances anomaly identification, resulting in safer and more effective operations for NPPs. |
| first_indexed | 2024-03-12T02:09:17Z |
| format | Article |
| id | doaj.art-8378735607264ac69e9e52d7ff49615d |
| institution | Directory Open Access Journal |
| issn | 2620-2832 2683-4111 |
| language | English |
| last_indexed | 2024-03-12T02:09:17Z |
| publishDate | 2023-08-01 |
| publisher | University of Kragujevac |
| record_format | Article |
| series | Proceedings on Engineering Sciences |
| spelling | doaj.art-8378735607264ac69e9e52d7ff49615d2023-09-06T15:39:08ZengUniversity of KragujevacProceedings on Engineering Sciences2620-28322683-41112023-08-015S1697810.24874/PES.SI.01.009DESIGNING AN IMPROVED NEURAL NETWORK FOR THE EARLY DETECTION OF ANOMALIES IN NUCLEAR POWER PLANTSSolomon Jebaraj0https://orcid.org/0000-0002-3385-207XDavendra Kumar Doda1https://orcid.org/0000-0002-8608-967XVineet Saxena2https://orcid.org/0000-0001-6491-9903Jain (deemed to be) University, Bangalore, IndiaVivekananda Global University, Jaipur, IndiaTeerthanker Mahaveer University, Moradabad, Uttar Pradesh, IndiaThe effectiveness and dependability of these vital energy infrastructures depend heavily on the early detection of anomalies in nuclear power plants (NPPs). Anomalies in a plant's operations might be signs of the impending equipment failure, a danger to workers' safety, or departure from ideal performance, all of which call for quick attention and preventative actions. Traditional NPP monitoring methods depend on the human inspections and predetermined thresholds, which are only sometimes successful in picking up the complicated irregularities. This Study introduces a new, Improved Bat and Grey Wolf Optimized Recurrent Neural Network (IBGWO-RNN) approach to detect the anomalies in NPPs. In this case, the RNN classification effectiveness is increased by using the IBGWO method. The American Nuclear Society ANSI / ANS-3.5 Nuclear Simulator Standard dataset has been used to assess the success of the suggested approach. Each input feature vector will be normalized by using the Z-score Normalization. A Kernel Principal Component Analysis (KPCA) is performed to extract the properties from segmented data. The results of the research show that the recommended methodology beats earlier approaches in terms of the Accuracy, Precision, Recall, and F1-score. Our suggested approach advances anomaly identification, resulting in safer and more effective operations for NPPs.https://pesjournal.net/journal/v5-nS1/9.pdfz-score normalizationkernel principal component analysis (kpca)nuclear power plants (npps)anomalyrecurrent neural network (rnn)bat and grey wolf optimized recurrent neural network (bgwo) |
| spellingShingle | Solomon Jebaraj Davendra Kumar Doda Vineet Saxena DESIGNING AN IMPROVED NEURAL NETWORK FOR THE EARLY DETECTION OF ANOMALIES IN NUCLEAR POWER PLANTS Proceedings on Engineering Sciences z-score normalization kernel principal component analysis (kpca) nuclear power plants (npps) anomaly recurrent neural network (rnn) bat and grey wolf optimized recurrent neural network (bgwo) |
| title | DESIGNING AN IMPROVED NEURAL NETWORK FOR THE EARLY DETECTION OF ANOMALIES IN NUCLEAR POWER PLANTS |
| title_full | DESIGNING AN IMPROVED NEURAL NETWORK FOR THE EARLY DETECTION OF ANOMALIES IN NUCLEAR POWER PLANTS |
| title_fullStr | DESIGNING AN IMPROVED NEURAL NETWORK FOR THE EARLY DETECTION OF ANOMALIES IN NUCLEAR POWER PLANTS |
| title_full_unstemmed | DESIGNING AN IMPROVED NEURAL NETWORK FOR THE EARLY DETECTION OF ANOMALIES IN NUCLEAR POWER PLANTS |
| title_short | DESIGNING AN IMPROVED NEURAL NETWORK FOR THE EARLY DETECTION OF ANOMALIES IN NUCLEAR POWER PLANTS |
| title_sort | designing an improved neural network for the early detection of anomalies in nuclear power plants |
| topic | z-score normalization kernel principal component analysis (kpca) nuclear power plants (npps) anomaly recurrent neural network (rnn) bat and grey wolf optimized recurrent neural network (bgwo) |
| url | https://pesjournal.net/journal/v5-nS1/9.pdf |
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