An internet of things enabled system for real-time monitoring and predictive maintenance of railway infrastructure

Gbadamosi, Abdul-Quayyum

Abstract

The railway industry plays a pivotal role in the socioeconomic landscape of many countries. However, its operation poses considerable challenges in terms of safety, environmental impact, and the intricacies of intertwined technical and social structures. Addressing these challenges necessitates the adoption of innovative approaches and advanced technologies. This doctoral research delves into the potential of the Internet of Things (IoT) as an enabler for railway infrastructure monitoring and predictive maintenance, aiming to enhance reliability, efficiency, and safety within the industry. Rooted in a pragmatic modelist philosophical stance, this thesis employs an exploratory sequential mixed-method approach incorporating qualitative and quantitative methodologies. The research process involves engaging with key stakeholders to gain insights into the challenges faced in railway maintenance and the opportunities presented by IoT implementation. Following this, an IoT system is developed, and a comprehensive value-creation framework is proposed for its effective implementation within the railway sector. The findings of this investigation underscore the transformative potential of IoT integration in railway infrastructure monitoring, yielding significant improvements in maintenance processes, safety, and operational efficiency. Furthermore, this doctoral research provides a foundation for future innovation and adaptation in the railway industry, contributing to its ongoing evolution and resilience in an ever-changing technological landscape.

• Thesis Type: Thesis
• Deposit Date: Jul 11, 2023
• Publicly Available Date: Oct 31, 2023
• Keywords: IoT, Railway, Maintenance, Infrastructure, Safety, Reliability, Predictive
• Public URL: https://uwe-repository.worktribe.com/output/10928540
• Citations for Published Sections: Gbadamosi, A.Q., Oyedele, L.O., Delgado, J.M.D., Kusimo, H., Akanbi, L., Olawale, O. and Muhammed-Yakubu, N., 2021. IoT for predictive assets monitoring and maintenance: An implementation strategy for the UK rail industry. Automation in Construction, 122,
• Award Date: Oct 31, 2023