This work presents a data-driven method for identifying rare functional dependencies among components of different systems of Complex Technical Infrastructures (CTIs) from large-scale databases of alarm messages. It is based on the representation of the alarm data in a binary form, the use of a novel association rule mining algorithm properly tailored for discovering rare dependencies among components of different systems and on the identification of groups of functionally dependent components. The proposed method is applied to a synthetic alarm database generated by a simulated CTI model and to a real large-scale database of alarms collected in the CTI of CERN (European Organization for Nuclear Research). The obtained results show the effectiveness of the proposed method.

A novel association rule mining method for the identification of rare functional dependencies in Complex Technical Infrastructures from alarm data

Antonello F.;Baraldi P.;Zio E.;
2021

Abstract

This work presents a data-driven method for identifying rare functional dependencies among components of different systems of Complex Technical Infrastructures (CTIs) from large-scale databases of alarm messages. It is based on the representation of the alarm data in a binary form, the use of a novel association rule mining algorithm properly tailored for discovering rare dependencies among components of different systems and on the identification of groups of functionally dependent components. The proposed method is applied to a synthetic alarm database generated by a simulated CTI model and to a real large-scale database of alarms collected in the CTI of CERN (European Organization for Nuclear Research). The obtained results show the effectiveness of the proposed method.
Abnormal behaviors
Alarm data
Association rules
Complex Technical Infrastructures
Rare functional dependencies
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1181139
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