DocumentCode :
1231443
Title :
The Use of Hidden Markov Models for Anomaly Detection in Nuclear Core Condition Monitoring
Author :
Stephen, Bruce ; West, Graeme M. ; Galloway, Stuart ; McArthur, Stephen D J ; Mcdonald, James R ; Towle, Dave
Author_Institution :
Inst. of Energy & Environ., Univ. of Strathclyde, Glasgow
Volume :
56
Issue :
2
fYear :
2009
fDate :
4/1/2009 12:00:00 AM
Firstpage :
453
Lastpage :
461
Abstract :
Unplanned outages can be especially costly for generation companies operating nuclear facilities. Early detection of deviations from expected performance through condition monitoring can allow a more proactive and managed approach to dealing with ageing plant. This paper proposes an anomaly detection framework incorporating the use of the Hidden Markov Model (HMM) to support the analysis of nuclear reactor core condition monitoring data. Fuel Grab Load Trace (FGLT) data gathered within the UK during routine refueling operations has been seen to provide information relating to the condition of the graphite bricks that comprise the core. Although manual analysis of this data is time consuming and requires considerable expertise, this paper demonstrates how techniques such as the HMM can provide analysis support by providing a benchmark model of expected behavior against which future refueling events may be compared. The presence of anomalous behavior in candidate traces is inferred through the underlying statistical foundation of the HMM which gives an observation likelihood averaged along the length of the input sequence. Using this likelihood measure, the engineer can be alerted to anomalous behaviour, indicating data which might require further detailed examination. It is proposed that this data analysis technique is used in conjunction with other intelligent analysis techniques currently employed to analyse FGLT to provide a greater confidence measure in detecting anomalous behaviour from FGLT data.
Keywords :
fission reactor core control; fission reactor fuel reprocessing; fission reactor monitoring; gas cooled reactors; graphite; hidden Markov models; nuclear reactor maintenance; FGLT data; Fuel Grab Load Trace data; advanced gas-cooled reactor; data analysis technique; data processing; fission reactor control; graphite bricks; hidden Markov models; nuclear power generation; nuclear reactor core condition monitoring data; refueling operations; unplanned outages; Aging; Condition monitoring; Data analysis; Fission reactors; Fuels; Hidden Markov models; Inductors; Inspection; Neutrons; Nuclear power generation; Data processing; fission reactor control and monitoring; hidden Markov models; nuclear; power generation;
fLanguage :
English
Journal_Title :
Nuclear Science, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9499
Type :
jour
DOI :
10.1109/TNS.2008.2011904
Filename :
4812284
Link To Document :
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