IoT for predictive assets monitoring and maintenance: An implementation strategy for the UK rail industry

Gbadamosi, Abdul; Oyedele, Lukumon; Davila Delgado, Juan Manuel; Kusimo, Habeeb; Akanbi, Lukman; Olawale, Oladimeji; Muhammed-Yakubu, Naimah

Abstract

With about 100% increase in rail service usage over the last 20 years, it is pertinent that rail infrastructure continues to function at an optimal level to avoid service disruptions, cancellations or delays due to unforeseen asset breakdown. In an endeavour to propose a strategy for the implementation of Internet of Things (IoT) in rail asset maintenance, a qualitative methodology was adopted through a series of focus-group workshops to identify the priority areas and enabling digital technologies for IoT implementation. The methods of data collection included audio recording, note- taking, and concept mapping. The audio records were transcribed and used for thematic analysis, while the concept maps were integrated for conceptual modelling and analysis. This paper presents an implementation strategy for IoT for rail assets maintenance with focus on priority areas such as real-time condition monitoring using IoT sensors, predictive maintenance, remote inspection, and integrated asset data management platform.

• Journal Article Type: Article
• Acceptance Date: Nov 18, 2020
• Online Publication Date: Dec 4, 2020
• Publication Date: Feb 1, 2021
• Deposit Date: Jan 27, 2021
• Publicly Available Date: Dec 5, 2021
• Print ISSN: 0926-5805
• Electronic ISSN: 1872-7891
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 122
• Article Number: 103486
• Public URL: https://uwe-repository.worktribe.com/output/6872140