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

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.

A Modified Generative Adversarial Network for Fault Diagnosis in High-Speed Train Components with Imbalanced and Heterogeneous Monitoring Data

“…For preventing this to occur, condition monitoring and fault diagnosis technologies are becoming increasingly applied to system health and safety management [2,3]. In particular with the digitization of industry and the associated availability of large amounts of data from system operation, data-driven methods are widely considered for fault diagnosis in complex systems [4,5].…”

Fault diagnosis for high‐speed train braking system based on disentangled causal representation learning

“…Due to the complexity in function and structure, it is very difficult or even impossible to gain a complete knowledge on its failures or to analyse the failure mechanisms of HST braking systems. In recent years, with the digitization of industry, data‐driven methods are widely considered for fault diagnosis (Chen & Jiang, 2020; Shokry et al, 2021).…”

Guest Editorial: Special Issue of ESREL2020 PSAM15

“…The following paper by Ahmed Shokry, Piero Baraldi, Andrea Castellano, Luigi Serio, and Enrico Zio proposes an intelligent approach for identifying critical components in critical infrastructures. This is a two-step process of feature selection and identification [3].…”