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@article{Lucke:2018:10.1016/j.ifacol.2018.09.324,
author = {Lucke, M and Chioua, M and Grimholt, C and Hollender, M and Thornhill, NF},
doi = {10.1016/j.ifacol.2018.09.324},
journal = {IFAC-PapersOnLine},
pages = {345--350},
title = {Online alarm flood classification using alarm coactivations},
url = {http://dx.doi.org/10.1016/j.ifacol.2018.09.324},
volume = {51},
year = {2018}
}

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TY  - JOUR
AB  - 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.
AU  - Lucke,M
AU  - Chioua,M
AU  - Grimholt,C
AU  - Hollender,M
AU  - Thornhill,NF
DO  - 10.1016/j.ifacol.2018.09.324
EP  - 350
PY  - 2018///
SN  - 2405-8963
SP  - 345
TI  - Online alarm flood classification using alarm coactivations
T2  - IFAC-PapersOnLine
UR  - http://dx.doi.org/10.1016/j.ifacol.2018.09.324
UR  - https://www.sciencedirect.com/science/article/pii/S2405896318320068
UR  - http://hdl.handle.net/10044/1/65389
VL  - 51
ER  -

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