Bibliographic Details
| Title: |
MFM-based alarm root-cause analysis and ranking for nuclear power plants |
| Authors: |
Mengchu Song, Christopher Reinartz, Xinxin Zhang, Harald P.-J. Thunem, Robert McDonald |
| Source: |
Nuclear Engineering and Technology, Vol 55, Iss 12, Pp 4408-4425 (2023) |
| Publisher Information: |
Elsevier, 2023. |
| Publication Year: |
2023 |
| Collection: |
LCC:Nuclear engineering. Atomic power |
| Subject Terms: |
Alarm analysis, Root-cause analysis, Root-cause ranking, Causal reasoning, MFM, Nuclear power plant, Nuclear engineering. Atomic power, TK9001-9401 |
| More Details: |
Alarm flood due to abnormality propagation is the most difficult alarm overloading problem in nuclear power plants (NPPs). Root-cause analysis is suggested to help operators in understand emergency events and plant status. Multilevel Flow Modeling (MFM) has been extensively applied in alarm management by virtue of the capability of explaining causal dependencies among alarms. However, there has never been a technique that can identify the actual root cause for complex alarm situations. This paper presents an automated root-cause analysis system based on MFM. The causal reasoning algorithm is first applied to identify several possible root causes that can lead to massive alarms. A novel root-cause ranking algorithm can subsequently be used to isolate the most likely faults from the other root-cause candidates. The proposed method is validated on a pressurized water reactor (PWR) simulator at HAMMLAB. The results show that the actual root cause is accurately identified for every tested operating scenario. The automation of root-cause identification and ranking affords the opportunity of real-time alarm analysis. It is believed that the study can further improve the situation awareness of operators in the alarm flooding situation. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
1738-5733 |
| Relation: |
http://www.sciencedirect.com/science/article/pii/S1738573323003509; https://doaj.org/toc/1738-5733 |
| DOI: |
10.1016/j.net.2023.07.034 |
| Access URL: |
https://doaj.org/article/8e7a34b43c6f406aac71431c085331ad |
| Accession Number: |
edsdoj.8e7a34b43c6f406aac71431c085331ad |
| Database: |
Directory of Open Access Journals |
