Detection of Historical Alarm Subsequences Using Alarm Events and a Coactivation Constraint

This paper aims to provide an in-depth study of the detection of historical alarm subsequences, which are frequently used as an initial step for alarm flood analysis methods. Therefore, state-of-the-art approaches are comprehensively examined, evaluated, and compared. To overcome the limitations of...

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Autores principales:	Gianluca Manca, Alexander Fay
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Abnormal situations alarm analysis alarm floods alarm management alarm systems Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/fdc2fa44524d4ac6b4e07c671440d980
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spelling	oai:doaj.org-article:fdc2fa44524d4ac6b4e07c671440d9802021-11-10T00:00:47ZDetection of Historical Alarm Subsequences Using Alarm Events and a Coactivation Constraint2169-353610.1109/ACCESS.2021.3067837https://doaj.org/article/fdc2fa44524d4ac6b4e07c671440d9802021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9382308/https://doaj.org/toc/2169-3536This paper aims to provide an in-depth study of the detection of historical alarm subsequences, which are frequently used as an initial step for alarm flood analysis methods. Therefore, state-of-the-art approaches are comprehensively examined, evaluated, and compared. To overcome the limitations of these methods, a novel approach is presented, which uses outlier detection in time distances between alarm events (activation and return to normal) and an alarm coactivation constraint. The effectiveness and performance of the examined methods are illustrated by means of an openly accessible dataset, which is introduced in this paper. It is based on the “Tennessee-Eastman-Process”, a benchmark in process automation. The intent is to provide a suitable dataset for the development and evaluation of alarm management methods in complex industrial processes using both quantitative and qualitative information from different sources. It is shown that the integration of supplementary information is beneficial for the overall performance and robustness of the detection method proposed here. This method allows for a more accurate detection of coherent historical abnormal situations, including phases with active root-cause disturbances and the normalization phases that follow their termination. Furthermore, the proposed method has the advantage that the detection results are less influenced by the alarm count, the propagation velocity, the duration of the situation, and the time distance between two causally independent situations in comparison to state-of-the-art approaches.Gianluca MancaAlexander FayIEEEarticleAbnormal situationsalarm analysisalarm floodsalarm managementalarm systemsElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 46851-46873 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Abnormal situations alarm analysis alarm floods alarm management alarm systems Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Abnormal situations alarm analysis alarm floods alarm management alarm systems Electrical engineering. Electronics. Nuclear engineering TK1-9971 Gianluca Manca Alexander Fay Detection of Historical Alarm Subsequences Using Alarm Events and a Coactivation Constraint
description	This paper aims to provide an in-depth study of the detection of historical alarm subsequences, which are frequently used as an initial step for alarm flood analysis methods. Therefore, state-of-the-art approaches are comprehensively examined, evaluated, and compared. To overcome the limitations of these methods, a novel approach is presented, which uses outlier detection in time distances between alarm events (activation and return to normal) and an alarm coactivation constraint. The effectiveness and performance of the examined methods are illustrated by means of an openly accessible dataset, which is introduced in this paper. It is based on the “Tennessee-Eastman-Process”, a benchmark in process automation. The intent is to provide a suitable dataset for the development and evaluation of alarm management methods in complex industrial processes using both quantitative and qualitative information from different sources. It is shown that the integration of supplementary information is beneficial for the overall performance and robustness of the detection method proposed here. This method allows for a more accurate detection of coherent historical abnormal situations, including phases with active root-cause disturbances and the normalization phases that follow their termination. Furthermore, the proposed method has the advantage that the detection results are less influenced by the alarm count, the propagation velocity, the duration of the situation, and the time distance between two causally independent situations in comparison to state-of-the-art approaches.
format	article
author	Gianluca Manca Alexander Fay
author_facet	Gianluca Manca Alexander Fay
author_sort	Gianluca Manca
title	Detection of Historical Alarm Subsequences Using Alarm Events and a Coactivation Constraint
title_short	Detection of Historical Alarm Subsequences Using Alarm Events and a Coactivation Constraint
title_full	Detection of Historical Alarm Subsequences Using Alarm Events and a Coactivation Constraint
title_fullStr	Detection of Historical Alarm Subsequences Using Alarm Events and a Coactivation Constraint
title_full_unstemmed	Detection of Historical Alarm Subsequences Using Alarm Events and a Coactivation Constraint
title_sort	detection of historical alarm subsequences using alarm events and a coactivation constraint
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/fdc2fa44524d4ac6b4e07c671440d980
work_keys_str_mv	AT gianlucamanca detectionofhistoricalalarmsubsequencesusingalarmeventsandacoactivationconstraint AT alexanderfay detectionofhistoricalalarmsubsequencesusingalarmeventsandacoactivationconstraint
_version_	1718440804606803968

Detection of Historical Alarm Subsequences Using Alarm Events and a Coactivation Constraint

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