Deep learning health management diagnostics applied to the NIST smoke experiments
Fire is one of the most important hazards that must be considered in advanced nuclear power plant safety assessments. The Nuclear Regulatory Commission (NRC) has developed a large collection of experimental data and associated analyses related to the study of fire safety. In fact, computational fire...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-04-01
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| Series: | Frontiers in Energy Research |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2023.1175102/full |
| _version_ | 1797843723564875776 |
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| author | Isaac Hoppman Saeed Alhadhrami Jun Wang |
| author_facet | Isaac Hoppman Saeed Alhadhrami Jun Wang |
| author_sort | Isaac Hoppman |
| collection | DOAJ |
| description | Fire is one of the most important hazards that must be considered in advanced nuclear power plant safety assessments. The Nuclear Regulatory Commission (NRC) has developed a large collection of experimental data and associated analyses related to the study of fire safety. In fact, computational fire models are based on quantitative comparisons to those experimental data. During the modeling process, it is important to develop diagnostic health management systems to check the equipment status in fire processes. For example, a fire sensor does not directly provide accurate and complex information that nuclear power plants (NPPs) require. With the assistance of the machine learning method, NPP operators can directly get information on local, ignition, fire material of an NPP fire, instead of temperature, smoke obscuration, gas concentration, and alarm signals. In order to improve the predictive capabilities, this work demonstrates how the deep learning classification method can be used as a diagnostic tool in a specific set of fire experiments. Through a single input from a sensor, the deep learning tool can predict the location and type of fire. This tool also has the capability to provide automatic signals to potential passive fire safety systems. In this work, test data are taken from a specific set of the National Institute of Standards and Technology (NIST) fire experiments in a residential home and analyzed by using the machine learning classification models. The networks chosen for comparison and evaluation are the dense neural networks, convolutional neural networks, long short-term memory networks, and decision trees. The dense neural network and long short-term memory network produce similar levels of accuracy, but the convolutional neural network produces the highest accuracy. |
| first_indexed | 2024-04-09T17:10:26Z |
| format | Article |
| id | doaj.art-8947a03dcd894769b4cab0c93dad9ba0 |
| institution | Directory Open Access Journal |
| issn | 2296-598X |
| language | English |
| last_indexed | 2024-04-09T17:10:26Z |
| publishDate | 2023-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Energy Research |
| spelling | doaj.art-8947a03dcd894769b4cab0c93dad9ba02023-04-20T05:59:26ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2023-04-011110.3389/fenrg.2023.11751021175102Deep learning health management diagnostics applied to the NIST smoke experimentsIsaac HoppmanSaeed AlhadhramiJun WangFire is one of the most important hazards that must be considered in advanced nuclear power plant safety assessments. The Nuclear Regulatory Commission (NRC) has developed a large collection of experimental data and associated analyses related to the study of fire safety. In fact, computational fire models are based on quantitative comparisons to those experimental data. During the modeling process, it is important to develop diagnostic health management systems to check the equipment status in fire processes. For example, a fire sensor does not directly provide accurate and complex information that nuclear power plants (NPPs) require. With the assistance of the machine learning method, NPP operators can directly get information on local, ignition, fire material of an NPP fire, instead of temperature, smoke obscuration, gas concentration, and alarm signals. In order to improve the predictive capabilities, this work demonstrates how the deep learning classification method can be used as a diagnostic tool in a specific set of fire experiments. Through a single input from a sensor, the deep learning tool can predict the location and type of fire. This tool also has the capability to provide automatic signals to potential passive fire safety systems. In this work, test data are taken from a specific set of the National Institute of Standards and Technology (NIST) fire experiments in a residential home and analyzed by using the machine learning classification models. The networks chosen for comparison and evaluation are the dense neural networks, convolutional neural networks, long short-term memory networks, and decision trees. The dense neural network and long short-term memory network produce similar levels of accuracy, but the convolutional neural network produces the highest accuracy.https://www.frontiersin.org/articles/10.3389/fenrg.2023.1175102/fullfirenuclear power plantsdeep learningclassificationlong short-term memory |
| spellingShingle | Isaac Hoppman Saeed Alhadhrami Jun Wang Deep learning health management diagnostics applied to the NIST smoke experiments Frontiers in Energy Research fire nuclear power plants deep learning classification long short-term memory |
| title | Deep learning health management diagnostics applied to the NIST smoke experiments |
| title_full | Deep learning health management diagnostics applied to the NIST smoke experiments |
| title_fullStr | Deep learning health management diagnostics applied to the NIST smoke experiments |
| title_full_unstemmed | Deep learning health management diagnostics applied to the NIST smoke experiments |
| title_short | Deep learning health management diagnostics applied to the NIST smoke experiments |
| title_sort | deep learning health management diagnostics applied to the nist smoke experiments |
| topic | fire nuclear power plants deep learning classification long short-term memory |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2023.1175102/full |
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