ECOC-based integrated learning method for fault diagnosis in nuclear power plants Article Swipe
Guimin Sheng
,
Yu Mu
,
Boyang Zhang
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.2478/amns.2023.2.00354
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.2478/amns.2023.2.00354
The fault diagnosis system of nuclear power plants plays an important role in ensuring the safety and economy of nuclear power plant operations. This paper first analyzes typical faults of nuclear power plants and their phenomena, and fault samples are obtained. A comprehensive study of the structure of the nuclear power plant system, its working mode and the association between each subsystem is carried out to analyze the monitoring parameters and fault characteristics and establish the fault data set. Secondly, an IFWA (Improved Fireworks Algorithm - Integrated Learning) algorithm is proposed to assess the severity of faults in the first circuit of a nuclear power plant. Finally, the fault diagnosis module is divided into three units according to the functional logic, i.e., condition monitoring unit, fault identification unit, and fault severity assessment unit. The results show that the diagnostic accuracy of the IFWA algorithm is 94.25% for SGTR in the single-fault diagnosis experiment and 96.25% for SGTR-LOCA in the multiple-fault diagnosis experiment. It shows that the IFWA algorithm proposed in this paper has the optimal performance capability when applied to nuclear power plant fault diagnosis and effectively assists managers in diagnosing faults and giving maintenance recommendations.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.2478/amns.2023.2.00354
- https://sciendo.com/pdf/10.2478/amns.2023.2.00354
- OA Status
- gold
- References
- 18
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4386955619
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4386955619Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.2478/amns.2023.2.00354Digital Object Identifier
- Title
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ECOC-based integrated learning method for fault diagnosis in nuclear power plantsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-09-20Full publication date if available
- Authors
-
Guimin Sheng, Yu Mu, Boyang ZhangList of authors in order
- Landing page
-
https://doi.org/10.2478/amns.2023.2.00354Publisher landing page
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https://sciendo.com/pdf/10.2478/amns.2023.2.00354Direct link to full text PDF
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YesWhether a free full text is available
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-
goldOpen access status per OpenAlex
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https://sciendo.com/pdf/10.2478/amns.2023.2.00354Direct OA link when available
- Concepts
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Nuclear power plant, Fault (geology), Reliability engineering, Nuclear power, Engineering, Computer science, Ecology, Geology, Physics, Seismology, Nuclear physics, BiologyTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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18Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.(Improved | 83 |
| abstract_inverted_index.Algorithm | 85 |
| abstract_inverted_index.Fireworks | 84 |
| abstract_inverted_index.Learning) | 88 |
| abstract_inverted_index.SGTR-LOCA | 157 |
| abstract_inverted_index.Secondly, | 80 |
| abstract_inverted_index.according | 117 |
| abstract_inverted_index.algorithm | 89, 144, 168 |
| abstract_inverted_index.condition | 123 |
| abstract_inverted_index.diagnosis | 3, 110, 152, 161, 185 |
| abstract_inverted_index.establish | 75 |
| abstract_inverted_index.important | 11 |
| abstract_inverted_index.obtained. | 41 |
| abstract_inverted_index.structure | 47 |
| abstract_inverted_index.subsystem | 62 |
| abstract_inverted_index.Integrated | 87 |
| abstract_inverted_index.assessment | 132 |
| abstract_inverted_index.capability | 177 |
| abstract_inverted_index.diagnosing | 191 |
| abstract_inverted_index.diagnostic | 139 |
| abstract_inverted_index.experiment | 153 |
| abstract_inverted_index.functional | 120 |
| abstract_inverted_index.monitoring | 69, 124 |
| abstract_inverted_index.parameters | 70 |
| abstract_inverted_index.phenomena, | 36 |
| abstract_inverted_index.association | 59 |
| abstract_inverted_index.effectively | 187 |
| abstract_inverted_index.experiment. | 162 |
| abstract_inverted_index.maintenance | 195 |
| abstract_inverted_index.operations. | 23 |
| abstract_inverted_index.performance | 176 |
| abstract_inverted_index.single-fault | 151 |
| abstract_inverted_index.comprehensive | 43 |
| abstract_inverted_index.identification | 127 |
| abstract_inverted_index.multiple-fault | 160 |
| abstract_inverted_index.characteristics | 73 |
| abstract_inverted_index.recommendations. | 196 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5109365092 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I55022517 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.49000000953674316 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile.value | 0.16252916 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |