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Lee, Seung Jun
Nuclear Safety Assessment and Plant HMI Evolution Lab.
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Deep learning-based procedure compliance check system for nuclear power plant emergency operation

Author(s)
Ahn, JeeyeaLee, Seung Jun
Issued Date
2020-12
DOI
10.1016/j.nucengdes.2020.110868
URI
https://scholarworks.unist.ac.kr/handle/201301/48667
Fulltext
https://www.sciencedirect.com/science/article/pii/S0029549320303629?via%3Dihub
Citation
NUCLEAR ENGINEERING AND DESIGN, v.370, pp.110868
Abstract
Operating procedures are strictly followed in nuclear power plant operation. However, under a highly stressful condition such as emergency operation, human error probability can increase, with operators making mistakes in complying with the complex operating procedures. This paper proposes a procedure compliance check (PCC) system to monitor operator action and detect procedural deviation. If an operator action does not match the related procedural instruction, the PCC system notifies the operator in order to help them to recognize the mistake. A procedural logic process is constructed by referring to colored Petri nets. In situations requiring complex decisions, the PCC system employs a deep learning algorithm to predict operator judgement. The system was tested with data from a compact nuclear simulator, and demonstrated its potential to detect procedural noncompliance.
Publisher
ELSEVIER SCIENCE SA
ISSN
0029-5493
Keyword (Author)
Emergency operationOperating procedureProcedure complianceOperator support systemComplex decision

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