Identification of Critical Components in the Complex Technical Infrastructure of the Large Hadron Collider Using Relief Feature Ranking and Support Vector Machines

Ahmed Shokry, Piero Baraldi, Andrea Castellano, Luigi Serio and Enrico Zio
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Ahmed Shokry: Center for Applied Mathematics, Ecole Polytechnique, Institut Polytechnique de Paris, Route de Saclay, 91120 Palaiseau, France
Piero Baraldi: Energy Department, Politecnico di Milano, Via Lambruschini 4, 20156 Milan, Italy
Andrea Castellano: Energy Department, Politecnico di Milano, Via Lambruschini 4, 20156 Milan, Italy
Luigi Serio: Engineering Department, CERN, 1211 Geneva, Switzerland
Enrico Zio: Energy Department, Politecnico di Milano, Via Lambruschini 4, 20156 Milan, Italy

Energies, 2021, vol. 14, issue 18, 1-19

Abstract: This work proposes a data-driven methodology for identifying critical components in Complex Technical Infrastructures (CTIs), for which the functional logic and/or the system structure functions are not known due the CTI’s complexity and evolving nature. The methodology uses large amounts of CTI monitoring data acquired over long periods of time and under different operating conditions. The critical components are identified as those for which the condition monitoring signals permit the optimal classification of the CTI functioning or failed state. The methodology includes two stages: in the first stage, a feature selection filter method based on the Relief technique is used to rank the monitoring signals according to their importance with respect to the CTI functioning or failed state; the second stage identifies the subset of signals among those highlighted by the Relief technique that are most informative with respect to the CTI state. This identification is performed on the basis of evaluating the performance of a Cost-Sensitive Support Vector Machine (CS-SVM) classifier trained with several subsets of the candidate signals. The capabilities of the methodology proposed are assessed through its application to different benchmarks of highly imbalanced datasets, showing performances that are competitive to those obtained by other methods presented in the literature. The methodology is finally applied to the monitoring signals of the Large Hadron Collider (LHC) of the European Organization for Nuclear Research (CERN), a CTI for experiments of physics; the criticality of the identified components has been confirmed by CERN experts.

Keywords: complex technical infrastructure; critical components; functional logic; feature ranking; Relief technique; filter methods; classification; support vectors machines; CERN; Large Hadron Collider (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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