Criteria-based alarm flood pattern recognition using historical data from automated production systems (aPS)

Vogel‐Heuser, Birgit; Schütz, Daniel; Folmer, Jens

doi:10.1016/j.mechatronics.2015.02.004

Cited by 54 publications

(18 citation statements)

References 21 publications

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“…Data-driven methods directly analyse, manage and reduce the alarm annunciation and therefore flooding [5], without a semantic representation of the system. Multiple approaches exist to this end, drawing from the data mining fields such as sequence identification and pattern recognition [6], [7], correlation analysis [8] or visualisation [9]. Many of these approaches utilise flood similarity measure of some kind, e.g., [10], [11].…”

Section: Introductionmentioning

confidence: 99%

Defining and validating similarity measures for industrial alarm flood analysis

Fullen¹,

Schüller

Niggemann³

2017

2017 IEEE 15th International Conference on Industrial Informatics (INDIN)

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Industrial plant operators regularly observe a high number of alarms generated in a short period of time, a phenomenon which is referred to as alarm flooding. This causes plant downtime, not only because of the repair time but also by the time needed to identify the root cause of machine failurewhich is difficult during an alarm flood. Therefore, diagnosis tools that perform root cause analysis to advise plant operators can help reduce the downtime, which is a crucial issue in industry. We analyse the reproducibility and applicability of an existing approach by Ahmed et al. (2013) which is based on agglomerative hierarchical clustering where raw data in the form of alarm logs is preprocessed, floods are detected, and then clustered. The aim is, that resulting clusters represent floods that originate from the same common root cause. We extend the approach with alternative similarity measures and perform experiments regarding their effectiveness in structuring industrial alarm flood data. In our evaluation we use a real industrial use case which contains more diverse data and a larger amount of data points compared with the original study.

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Section: Introductionmentioning

confidence: 99%

Defining and validating similarity measures for industrial alarm flood analysis

Fullen¹,

Schüller

Niggemann³

2017

2017 IEEE 15th International Conference on Industrial Informatics (INDIN)

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“…Die zugrundeliegenden Methoden und Ansätze können hierbei an unterschiedlichsten Stellen in der Prozessindustrie eingesetzt werden. So befassen sich die aktuellen Forschungsprojekte Frühzeitige Erkennung und Entscheidungsunterstützung für kritische Situationen im Produktionsumfeld (FEE) beziehungsweise Skalierbares Integrationskonzept zur Datenaggregation, -analyse, -aufbereitung von großen Datenmengen in der Prozessindustrie (Sidap) mit der Vorhersage von Geräteausfällen in chemischen Anlagen, der Analyse von Alarmschauern [1] und der Vorhersage der Prozessqualität, um Assistenzsysteme zur besseren Führung der Anlagen anbieten zu können.…”

Section: Big Und Smart Dataunclassified

Big und Smart Data

Folmer¹,

Kirchen²,

Trunzer³

et al. 2017

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In der Prozessindustrie fallen eine Vielzahl unterschiedlicher, heterogener Daten an, und das Gesamtsystem kann aufgrund seiner Komplexität und Dynamik nicht komplett formal beschrieben werden. Daher untersuchen die Projekte Sidap und FEE die Eignung von Big- Data- und Smart-Data-Ansätzen in dieser Domäne. Obwohl beide Projekte unterschiedliche Ansätze verfolgen, ergeben sich gemeinsame Herausforderungen. Dieser Beitrag fasst diese zusammen und zeigt Lösungsansätze auf, beispielsweise durch die Schaffung eines gemeinsamen Datenverständnisses oder die Anreicherung der Daten mit Zusatzinformation.

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“…Data-driven methods directly analyse, manage and reduce the alarm annunciation and therefore flooding [2], without a semantic representation of the system. Multiple approaches exist to this end, drawing from the data mining fields such as sequence identification and pattern recognition [18,4], correlation analysis [21] or visualisation [13]. Many of these approaches utilise flood similarity measure of some kind, e.g., [20,3].…”

Section: Introductionmentioning

confidence: 99%

Validation of similarity measures for industrial alarm flood analysis?

Fullen

Schüller

Niggemann

2018

IMPROVE - Innovative Modelling Approaches for Production Systems to Raise Validatable Efficiency

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Abstract. The aim of industrial alarm flood analysis is to assist plant operators who face large amounts of alarms, referred to as alarm floods, in their daily work. Many methods used to this end involve some sort of a similarity measure to detect similar alarm sequences. However, multiple similarity measures exist and it is not clear which one is best suited for alarm analysis. In this paper, we perform an analysis of the behaviour of the similarity measures and attempt to validate the results in a semi-formalised way. To do that, we employ synthetically generated floods, based on assumption that synthetic floods that are generated as 'similar' to the original floods should receive similarity scores close to the original floods. Consequently, synthetic floods generated as 'not-similar' to the original floods are expected to receive different similarity scores. Validation of similarity measures is performed by comparing the result of clustering the original and synthetic alarm floods. This comparison is performed with standard clustering validation measures and application-specific measures.

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Criteria-based alarm flood pattern recognition using historical data from automated production systems (aPS)

Cited by 54 publications

References 21 publications

Defining and validating similarity measures for industrial alarm flood analysis

Defining and validating similarity measures for industrial alarm flood analysis

Big und Smart Data

Validation of similarity measures for industrial alarm flood analysis?

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