This work proposes a methodology for identifying dependent abnormal behaviours through the extraction of association rules from data. The practical case considered makes use of a database of alarms generated by different supervision systems of the CERN (European Centre for Nuclear Research) technical infrastructure. The methodology is based on the representation of the alarm database with a binary matrix and the use of the Apriori algorithm for mining association rules. An application to a large-scale database of alarms generated by various monitoring systems of the point 8 of CERN is presented.

Data-driven extraction of association rules of dependent abnormal behaviour groups

Antonello F.;Baraldi P.;Zio E.;
2020

Abstract

This work proposes a methodology for identifying dependent abnormal behaviours through the extraction of association rules from data. The practical case considered makes use of a database of alarms generated by different supervision systems of the CERN (European Centre for Nuclear Research) technical infrastructure. The methodology is based on the representation of the alarm database with a binary matrix and the use of the Apriori algorithm for mining association rules. An application to a large-scale database of alarms generated by various monitoring systems of the point 8 of CERN is presented.
Proceedings of the 29th European Safety and Reliability Conference, ESREL 2019
978-981-11-2724-3
Alarm Database Representation
Alarms
Association Rules
Complex Technical Infrastructures
Data Mining
Dependent Abnormal Behaviours
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1160359
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