A novelty-based multi-objective evolutionary algorithm for identifying functional dependencies in complex technical infrastructures from alarm data

Federico Antonello, Piero Baraldi (), Enrico Zio and Luigi Serio
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Federico Antonello: Politecnico di Milano
Piero Baraldi: Politecnico di Milano
Enrico Zio: Politecnico di Milano
Luigi Serio: CERN

Environment Systems and Decisions, 2022, vol. 42, issue 2, 177-188

Abstract: Abstract In this work, a Multi-Objective Evolutionary Algorithm (MOEA) is developed to identify Functional Dependencies (FDEPs) in Complex Technical Infrastructures (CTIs) from alarm data. The objectives of the search are the maximization of a measure of novelty, which drives the exploration of the solution space avoiding to get trapped in local optima, and of a measure of dependency among alarms, which drives the uncovering of functional dependencies. The main contribution of the work is the direct identification of patterns of dependent alarms; this avoids going through the preliminary step of mining association rules, as typically done by state-of-the-art methods which, however, fail to identify rare functional dependencies due to the need of setting a balanced minimum occurrence threshold. The proposed framework for FDEPs identification is applied to a synthetic alarm database generated by a simulated CTI model and to a real large-scale database of alarms collected at the CTI of CERN (European Organization for Nuclear Research). The obtained results show that the framework enables the thorough exploration of the solution space and captures also rare functional dependencies.

Keywords: Complex technical infrastructures; Functional dependencies; Alarm data; Abnormal behaviours; Multi-objective evolutionary algorithm; Genetic algorithm; Novelty search; Particle accelerator (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10669-021-09841-z

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