Complex technical infrastructures are systems-of-systems characterized by hierarchical structures, made by thousands of interconnected components performing different functions associated to various domains. Given the difficulty of deriving their functional logic using traditional risk and reliability analysis methods, we address the problem of critical component identification from an innovative perspective, which exploits the large amount of available monitored data of operation. Specifically, we develop a data-driven framework of analysis which employs Bayesian additive regression trees and validate it on a synthetic case study, which mimics the complexity of a complex technical infrastructure.

Data-driven identification of critical components in complex technical infrastructures using Bayesian additive regression trees

Lu X.;Antonello F.;Baraldi P.;Zio E.
2020

Abstract

Complex technical infrastructures are systems-of-systems characterized by hierarchical structures, made by thousands of interconnected components performing different functions associated to various domains. Given the difficulty of deriving their functional logic using traditional risk and reliability analysis methods, we address the problem of critical component identification from an innovative perspective, which exploits the large amount of available monitored data of operation. Specifically, we develop a data-driven framework of analysis which employs Bayesian additive regression trees and validate it on a synthetic case study, which mimics the complexity of a complex technical infrastructure.
Proceedings of the 29th European Safety and Reliability Conference, ESREL 2019
978-981-11-2724-3
Bayesian Additive Regression Trees
Complex Technical Infrastructure
Critical Components Identification
Feature Selection
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1160357
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