ЦИФРОВІ ТЕХНОЛОГІЇ ПІДТРИМКИ ЖИТТЄВОГО ЦИКЛУ ЗАЛІЗНИЧНОГО РУХОМОГО СКЛАДУ: АНАЛІТИКА ТА ВЕКТОРИ РОЗВИТКУ

D.I. Skurikhin; A.O. Lovska; V.H. Ravlyuk; A.V. Rybіn

doi:10.33082/td.2025.3-26.08

D.I. Skurikhin Ukrainian State University of Railway Transport https://orcid.org/0000-0002-3746-5157
A.O. Lovska Ukrainian State University of Railway Transport https://orcid.org/0000-0002-8604-1764
V.H. Ravlyuk Ukrainian State University of Railway Transport https://orcid.org/0000-0003-4818-9482
A.V. Rybіn Ukrainian State University of Railway Transport https://orcid.org/0000-0003-4430-8018

DOI: https://doi.org/10.33082/td.2025.3-26.08

Keywords: lifecycle, rolling stock, digital twin, railcar manufacturing, maintenance, blockchain, 3D printing, PLM, IoT

Abstract

Introduction. Numerous scientific publications emphasize the importance of integrating modern digital technologies into the management of technical systems, particularly in the railway sector. However, the specifics of the railway rolling stock lifecycle require a comprehensive approach that combines engineering solutions, information technologies, organizational processes, and safety requirements. Recent literature increasingly addresses the need to adapt such integrated models to discrete manufacturing industries with a high level of regulation, such as railcar production. Purpose. This article expands the analysis of integrated lifecycle management technologies for railway rolling stock, covering all stages from design to disposal. The objective is to systematize current digital tools, evaluate their impact on efficiency, safety, and process transparency, and identify directions for adaptation in countries with transition economies. Results. A wide range of technologies used at each lifecycle stage of railcars has been analyzed. The design stage relies on PLM systems, CAD/CAE platforms, and simulation tools for loads and lifecycle assessment. The manufacturing stage is supported by MES, ERP, RFID/Barcode systems, additive manufacturing, and AI-based quality control, with real-world examples from SNCF, Alstom, and CAF. The operation stage incorporates IoT sensors, digital twins, telematics, fleet management, and big data analytics. Predictive maintenance is enabled through AI, smart contracts, and blockchain-based service history records. Modernization involves PLM integration, 3D scanning, obsolescence management, and strategic replacement of components. The final stage – disposal and recycling – is supported by life cycle assessment, material tracking, and recyclability metrics. Special focus is given to cloud-based blockchain infrastructure and smart contracts that connect all lifecycle phases into a single trusted digital environment. Conclusions. This study demonstrates that integrated lifecycle management of railway rolling stock is essential for increasing efficiency, safety, and service life. Digital technologies enable cost reduction, faster production and maintenance cycles, and complete component traceability. Further research should focus on the localization of Smart MES systems, blockchain integration in maintenance workflows, and the creation of digital component archives for effective modernization. The paper also highlights the need to standardize data management practices and adapt global solutions to the specific context of railcar manufacturing in transition economies.

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DIGITAL TECHNOLOGIES IN THE LIFECYCLE MANAGEMENT OF RAILWAY ROLLING STOCK: ANALYTICAL OVERVIEW AND DEVELOPMENT PATHS

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