Name
Abstract | ||
|---|---|---|
Nowadays, automation has been generalized with artificial intelligences in many areas. In nuclear power plants, some features which have simple logics in nuclear power plants such as reactor trip and engineered safety features (ESFs) actuation have been automated, whereas, other components have not been automated yet, so human operators are still necessary to control the reactor in emergency or abnormal situations. However, there exists a risk of human errors since human operators are involved in nuclear power operations. That is because, human error may contribute to the risk of severe accidents. To reduce those human errors, moreover, to draw to extend the portion of automation in nuclear power plants, a framework which automatically detects Unsafe Acts (UAs) which are occurred in advanced main control rooms of nuclear power plants has been introduced. Human operators are supposed to operate nuclear power plants by following operating procedures. However, in real operational situation, they violate operating procedures sometimes to achieve the goal (to keep the plant integrity) based on their own experiences and their know-hows. Critical safety functions (CSFs) can disentangle whether an operator's action will adversely affect plant integrity. Thus, the UA autodetection system considers both procedure violation and CSFs violation to find out errors made by human operator. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-319-92270-6_48 | Communications in Computer and Information Science |
Keywords | Field | DocType |
Unsafe act,Autodetection,Nuclear power plant operation | Human operator,Existential quantification,Computer science,Automation,Risk analysis (engineering),Human error,Human–computer interaction,Operator (computer programming),Nuclear power plant,Nuclear power | Conference |
Volume | ISSN | Citations |
850 | 1865-0929 | 0 |
PageRank | References | Authors |
0.34 | 1 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jeeyea Ahn | 1 | 0 | 0.34 |
| Jae Min Kim | 2 | 22 | 5.21 |
| Seung Jun Lee | 3 | 1 | 2.74 |