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Abnormal state diagnosis model tolerant to noise in plant data
- Source :
- Nuclear Engineering and Technology, Vol 53, Iss 4, Pp 1181-1188 (2021)
- Publication Year :
- 2021
- Publisher :
- Elsevier, 2021.
-
Abstract
- When abnormal events occur in a nuclear power plant, operators must conduct appropriate abnormal operating procedures. It is burdensome though for operators to choose the appropriate procedure considering the numerous main plant parameters and hundreds of alarms that should be judged in a short time. Recently, various research has applied deep-learning algorithms to support this problem by classifying each abnormal condition with high accuracy. Most of these models are trained with simulator data because of a lack of plant data for abnormal states, and as such, developed models may not have tolerance for plant data in actual situations. In this study, two approaches are investigated for a deep-learning model trained with simulator data to overcome the performance degradation caused by noise in actual plant data. First, a preprocessing method using several filters was employed to smooth the test data noise, and second, a data augmentation method was applied to increase the acceptability of the untrained data. Results of this study confirm that the combination of these two approaches can enable high model performance even in the presence of noisy data as in real plants.
- Subjects :
- Computer science
neural network
020209 energy
Operating procedures
ComputerApplications_COMPUTERSINOTHERSYSTEMS
02 engineering and technology
computer.software_genre
030218 nuclear medicine & medical imaging
law.invention
03 medical and health sciences
0302 clinical medicine
law
Nuclear power plant
0202 electrical engineering, electronic engineering, information engineering
Preprocessor
Noisy data
Abnormal operating procedure
Accident diagnosis
lcsh:TK9001-9401
Noise
Nuclear Energy and Engineering
lcsh:Nuclear engineering. Atomic power
Data mining
State (computer science)
computer
Test data
Degradation (telecommunications)
Subjects
Details
- Language :
- English
- ISSN :
- 17385733
- Volume :
- 53
- Issue :
- 4
- Database :
- OpenAIRE
- Journal :
- Nuclear Engineering and Technology
- Accession number :
- edsair.doi.dedup.....91ecf6bc6cb368c4d85d7e33e0203a37