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Keywords = asset attribute characterization (AAC)

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24 pages, 2974 KB  
Article
Digitalization and Dynamic Criticality Analysis for Railway Asset Management
by Mauricio Rodríguez Hernández, Antonio Sánchez-Herguedas, Vicente González-Prida, Sebastián Soto Contreras and Adolfo Crespo Márquez
Appl. Sci. 2024, 14(22), 10642; https://doi.org/10.3390/app142210642 - 18 Nov 2024
Cited by 1 | Viewed by 2454
Abstract
The primary aim of this paper is to support the optimization of asset management in railway infrastructure through digitalization and criticality analysis. It addresses the current challenges in railway infrastructure management, where data-driven decision making and automation are key for effective resource allocation. [...] Read more.
The primary aim of this paper is to support the optimization of asset management in railway infrastructure through digitalization and criticality analysis. It addresses the current challenges in railway infrastructure management, where data-driven decision making and automation are key for effective resource allocation. The paper presents a methodology that emphasizes the development of a robust data model for criticality analysis, along with the advantages of integrating advanced digital tools. A master table is designed to rank assets and automatically calculate criticality through a novel asset attribute characterization (AAC) process. Digitalization facilitates dynamic, on-demand criticality assessments, which are essential in managing complex networks. The study also underscores the importance of combining digital technology adoption with organizational change management. The data process and structure proposed can be viewed as an ontological framework adaptable to various contexts, enabling more informed and efficient asset ranking decisions. This methodology is derived from its application to a metropolitan railway network, where thousands of assets were evaluated, providing a practical approach for conducting criticality assessments in a digitized environment. Full article
(This article belongs to the Special Issue Big-Data-Driven Advances in Smart Maintenance and Industry 4.0)
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