Clustering of Similar Historical Alarm Subsequences in Industrial Control Systems Using Alarm Series and Characteristic Coactivations

Alarm flood similarity analysis (AFSA) methods are frequently used as a primary step for root-cause analysis, alarm flood pattern mining, and online operator support. AFSA methods have been promoted in several research activities in recent years. However, addressing an often-observed ambiguity of th...

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Autores principales:	Gianluca Manca, Marcel Dix, Alexander Fay
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Abnormal situations alarm analysis alarm floods alarm management industrial alarm systems industrial process diagnosis Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/141d40dec0854c39b15ace30a9ac041e
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spelling	oai:doaj.org-article:141d40dec0854c39b15ace30a9ac041e2021-11-30T00:00:13ZClustering of Similar Historical Alarm Subsequences in Industrial Control Systems Using Alarm Series and Characteristic Coactivations2169-353610.1109/ACCESS.2021.3128695https://doaj.org/article/141d40dec0854c39b15ace30a9ac041e2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9617607/https://doaj.org/toc/2169-3536Alarm flood similarity analysis (AFSA) methods are frequently used as a primary step for root-cause analysis, alarm flood pattern mining, and online operator support. AFSA methods have been promoted in several research activities in recent years. However, addressing an often-observed ambiguity of the order of alarms and the annunciation of irrelevant alarms in otherwise similar alarm subsequences remains a challenging task. To address and solve these limitations, this paper presents a novel AFSA method that uses alarm series as input to two extended term frequency-inverse document frequency (TF-IDF)-based clustering approaches, a dimensionality reduction technique, and a novel outlier validation. The method proposed here utilizes both characteristic alarm variables and their coactivations, thus, emphasizing the dynamic properties of alarms to a greater extent. Our method is compared to three relevant methods from the literature. The effectiveness and performance of the examined methods are illustrated by means of an openly accessible dataset based on the “Tennessee-Eastman-Process”. It is shown that the integration of alarm series data improves the overall performance and robustness of the AFSA. Furthermore, the clustering results are less influenced by the ambiguity of the order of alarms and irrelevant alarms, thus overcoming a persistent challenge in alarm management research.Gianluca MancaMarcel DixAlexander FayIEEEarticleAbnormal situationsalarm analysisalarm floodsalarm managementindustrial alarm systemsindustrial process diagnosisElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 154965-154974 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Abnormal situations alarm analysis alarm floods alarm management industrial alarm systems industrial process diagnosis Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Abnormal situations alarm analysis alarm floods alarm management industrial alarm systems industrial process diagnosis Electrical engineering. Electronics. Nuclear engineering TK1-9971 Gianluca Manca Marcel Dix Alexander Fay Clustering of Similar Historical Alarm Subsequences in Industrial Control Systems Using Alarm Series and Characteristic Coactivations
description	Alarm flood similarity analysis (AFSA) methods are frequently used as a primary step for root-cause analysis, alarm flood pattern mining, and online operator support. AFSA methods have been promoted in several research activities in recent years. However, addressing an often-observed ambiguity of the order of alarms and the annunciation of irrelevant alarms in otherwise similar alarm subsequences remains a challenging task. To address and solve these limitations, this paper presents a novel AFSA method that uses alarm series as input to two extended term frequency-inverse document frequency (TF-IDF)-based clustering approaches, a dimensionality reduction technique, and a novel outlier validation. The method proposed here utilizes both characteristic alarm variables and their coactivations, thus, emphasizing the dynamic properties of alarms to a greater extent. Our method is compared to three relevant methods from the literature. The effectiveness and performance of the examined methods are illustrated by means of an openly accessible dataset based on the “Tennessee-Eastman-Process”. It is shown that the integration of alarm series data improves the overall performance and robustness of the AFSA. Furthermore, the clustering results are less influenced by the ambiguity of the order of alarms and irrelevant alarms, thus overcoming a persistent challenge in alarm management research.
format	article
author	Gianluca Manca Marcel Dix Alexander Fay
author_facet	Gianluca Manca Marcel Dix Alexander Fay
author_sort	Gianluca Manca
title	Clustering of Similar Historical Alarm Subsequences in Industrial Control Systems Using Alarm Series and Characteristic Coactivations
title_short	Clustering of Similar Historical Alarm Subsequences in Industrial Control Systems Using Alarm Series and Characteristic Coactivations
title_full	Clustering of Similar Historical Alarm Subsequences in Industrial Control Systems Using Alarm Series and Characteristic Coactivations
title_fullStr	Clustering of Similar Historical Alarm Subsequences in Industrial Control Systems Using Alarm Series and Characteristic Coactivations
title_full_unstemmed	Clustering of Similar Historical Alarm Subsequences in Industrial Control Systems Using Alarm Series and Characteristic Coactivations
title_sort	clustering of similar historical alarm subsequences in industrial control systems using alarm series and characteristic coactivations
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/141d40dec0854c39b15ace30a9ac041e
work_keys_str_mv	AT gianlucamanca clusteringofsimilarhistoricalalarmsubsequencesinindustrialcontrolsystemsusingalarmseriesandcharacteristiccoactivations AT marceldix clusteringofsimilarhistoricalalarmsubsequencesinindustrialcontrolsystemsusingalarmseriesandcharacteristiccoactivations AT alexanderfay clusteringofsimilarhistoricalalarmsubsequencesinindustrialcontrolsystemsusingalarmseriesandcharacteristiccoactivations
_version_	1718406846032642048

Clustering of Similar Historical Alarm Subsequences in Industrial Control Systems Using Alarm Series and Characteristic Coactivations

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