Fault detection and diagnosis in IP-based mission critical industrial process control networks
- Authors
- Won, Young J.; Choi, Mi-Jung; Hong, James Won-Ki; Kim, Myung-Sup; Hwang, Hwawon; Lee, Jun-Hyub; Lee, Sung-Gyoo
- Issue Date
- 5월-2008
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- Fault detection; IP networks; Maintenance engineering; Monitoring; Plants (biology); Process control; Software
- Citation
- IEEE COMMUNICATIONS MAGAZINE, v.46, no.5, pp.172 - 180
- Indexed
- SCIE
SCOPUS
- Journal Title
- IEEE COMMUNICATIONS MAGAZINE
- Volume
- 46
- Number
- 5
- Start Page
- 172
- End Page
- 180
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/123660
- DOI
- 10.1109/MCOM.2008.4511666
- ISSN
- 0163-6804
- Abstract
- Mission-critical industrial process control networks support secure and reliable communications of devices in a controlling or manufacturing environment. They used to mostly use proprietary protocols and networks. Recently, however, many of them are being migrated to IP-based networks to consolidate many different types of networks into a single common network to simplify network operation, administration, and maintenance, and reduce operational expenses and capital expenditures. Despite their wide deployment, most operators have very little knowledge on how to operate them reliably and securely. This is mainly due to the operators' unfamiliarity with various faults that occur on IP-based process control networks. The current process of detecting and diagnosing faults in process control networks is mostly manual and thus the operators detect the problems only after noticeable process malfunctions. This article presents an overview of industrial process control networks and discusses the issues of introducing IP technologies into them. We then propose a fault detection and diagnosis method which is suitable for IP-based process control networks. We also present the system architecture and implementation of fault detection and diagnosis system as well as its deployment at POSCO. Finally, based on operational experience, we have generated a failure prediction model that can be used to predict potential alarms.
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Collections - Graduate School > Department of Computer and Information Science > 1. Journal Articles
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