Towards Smart Maintenance

The Implementation of Predictive Maintenance in the Railway Industry

Master thesis (2024)

Authors

K. Foeken Technology, Policy and Management

Contributors

P.H.A.J.M. van Gelder (graduation committee member)
N. Mouter Transport and Logistics - TPM (graduation committee member)
Z. Li Railway Engineering - CEG (graduation committee member)
Thijs Blad (mentor)
Faculty
Technology, Policy and Management
More Info
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Published Date

20-12-2024

Language

English

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Faculty

Technology, Policy and Management

Abstract

The thesis titled "Towards Smart Maintenance: The Implementation of Predictive Maintenance in the Railway Industry" focuses on integrating predictive maintenance (PdM) technologies into railway operations, emphasizing freight rail. PdM employs data analytics, machine learning, and IoT-enabled sensors to shift from traditional maintenance approaches to a proactive, data-driven methodology. This transformation promises reduced downtime, optimized resource use, and enhanced system reliability.

The research identifies technical, financial, and organizational challenges as key barriers to PdM adoption, including data standardization, algorithm maturity, high initial investment, and resistance to change. Drawing lessons from industries like aviation and infrastructure, the study highlights phased implementation, regulatory frameworks, and stakeholder collaboration as critical enablers.

Employing tools like Interpretive Structural Modeling and Fuzzy MICMAC, the thesis maps interdependencies among barriers and demonstrates PdM's potential for reducing maintenance costs by up to 47.8% over two decades. It concludes with recommendations for systematic adoption, addressing both technical and managerial factors, to ensure safer and more efficient railway maintenance practices.

Files

Thesis_report_Kiet_Foeken_.pdf
(pdf | 10.5 Mb)
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