banner.jpg 0723-cd Data-Driven Extraction of Association Rules of Dependent Abnormal Behaviour Groups Federico Antonello1, Piero Baraldi1,a, Ahmed Shokry1, Enrico Zio1,2, Ugo Gentile3 and Luigi Serio3 1Energy Department, Politecnico di Milano, Via Lambruschini, Milan, Italy apiero.baraldi@polimi.it 2MINES ParisTech, PSL Research University, CRC, Sophia Antipolis, France.
Eminent Scholar, Department of Nuclear Engineering, College of Engineering, Kyung Hee University, Republic of Korea 3CERN, 1211 Geneva 23, Switzerland 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.

Keywords: Complex Technical Infrastructures, Dependent Abnormal Behaviours, Association Rules, Alarms, Data Mining, Alarm Database Representation.

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