Online alarm flood classification using alarm coactivations
File(s)LuckeEtAl_OnlineAlarmFlood_ADCHEM2018_Accepted.pdf (983.74 KB)
Accepted version
Author(s)
Lucke, Matthieu
Chioua, Moncef
Grimholt, Chriss
Hollender, Martin
Thornhill, Nina F
Type
Journal Article
Abstract
Alarms indicate abnormal operation of the process plants and alarm floods constitute specific abnormal episodes that cannot be handled safely by the operators. In that regard, online alarm flood classification based on a bank of past historical episodes provides support on how to handle ongoing alarm sequences. This paper introduces a new approach based on alarm coactivations that is appropriate for the analysis of ongoing sequences. The method shows improvements when compared to an established sequence alignment approach for abnormal episode analysis of a gas oil separation plant.
Date Issued
2018-07-25
Date Acceptance
2018-03-23
Citation
IFAC-PapersOnLine, 2018, 51 (18), pp.345-350
ISSN
2405-8963
Publisher
IFAC Secretariat
Start Page
345
End Page
350
Journal / Book Title
IFAC-PapersOnLine
Volume
51
Issue
18
Copyright Statement
© 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. This manuscript is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence http://creativecommons.org/licenses/by-nc-nd/4.0/
Sponsor
Commission of the European Communities
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000446604800060&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Grant Number
675215
Subjects
Alarm systems
Fault detection and diagnosis
Alarm flood analysis
SIMILARITY ANALYSIS
SEQUENCES
Publication Status
Published
Coverage Spatial
Shenyang, PEOPLES R CHINA
Date Publish Online
2018-10-08