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Nowhere to Hide Methodology: Application of Clustering Fault Diagnosis in the Nuclear Power Industry
- Source :
- IEEE Access, Vol 7, Pp 179864-179879 (2019)
- Publication Year :
- 2019
- Publisher :
- IEEE, 2019.
-
Abstract
- When a system crashes, fast and accurate log-based fault diagnosis can remarkably reduce the recovery time of the system and avoid further economic losses. Especially for the nuclear power industry, recovery time will lead not only to economic losses but also to international repercussions. Nevertheless, the massive quantity of obscure log information and the existence of hidden nodes pose major challenges to fault diagnosis and root cause determination. To overcome these obstacles, we propose the nowhere to hide (NTH) methodology, an efficient method to diagnose faults and locate root causes. We implement log-node and node-log mapping to avoid vital data loss in collecting fault logs and hidden nodes; furthermore, we utilize the logic of the nuclear power unit process system to reveal the crucial information in fault logs and hidden nodes and their causality to determine the root cause. We evaluate the methodology in a real nuclear industrial environment. The results show that system administrators can efficiently determine the root cause with the proposed methodology. Finally, we discuss the enhancements that are underway to improve the methodology.
- Subjects :
- Root (linguistics)
General Computer Science
detectable target nodes (DTNs)
Computer science
020209 energy
key log
02 engineering and technology
Data loss
hidden nontarget nodes (HNNs)
Fault (power engineering)
hidden target nodes (HTNs)
01 natural sciences
010305 fluids & plasmas
Causality (physics)
0103 physical sciences
0202 electrical engineering, electronic engineering, information engineering
General Materials Science
Cluster analysis
distributed control system (DCS)
Unit process
business.industry
General Engineering
Nuclear power
Root cause
Reliability engineering
lcsh:Electrical engineering. Electronics. Nuclear engineering
business
lcsh:TK1-9971
Clustering fault diagnosis
Subjects
Details
- Language :
- English
- ISSN :
- 21693536
- Volume :
- 7
- Database :
- OpenAIRE
- Journal :
- IEEE Access
- Accession number :
- edsair.doi.dedup.....4adec487666fb93d3130cd42de767379