A novel association rule mining method for the identification of rare functional dependencies in Complex Technical Infrastructures from alarm data. (15th May 2021)

• Record Type: Journal Article
• Authors: Antonello, Federico; Baraldi, Piero; Shokry, Ahmed; Zio, Enrico; Gentile, Ugo; Serio, Luigi
• Is Part Of: Expert systems with applications. Volume 170(2021)
• Publication Date: 2021-05-15
• Subjects: Complex Technical Infrastructures -- Rare functional dependencies -- Association rules -- Alarm data -- Abnormal behaviors; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2021.114560
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: We propose a data-driven method for identifying rare functional dependencies. The method is tailored for Complex Technical Infrastructures (CTIs). The method is based on a novel association rule mining approach. The method reduces the computational effort required to identify rare dependencies. The method is validated considering a real dataset from the CTI of CERN. 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.