Abstract Railway infrastructure includes complex, integrated, multi-disciplinary systems requiring regular inspections and maintenance. Track and Overhead Line Equipment (OLE) are the main railway infrastructure systems. These systems deteriorate over time due to operations and environmental factors. Monitoring technologies are used to replace manual measurements, to gather data relating to the condition of railway infrastructure assets. Engineers and experts review and make decisions. There is a collective interest to replace the current reactive and preventative maintenance with Predictive Maintenance (PdM) which means maintaining or renewing assets before they fail. This is achieved by condition monitoring of assets. The research data is collected using a Remote Condition Monitoring (RCM) system mounted on a tram on the UK’s biggest light rail network. Data is processed by analytical software. The research aims to introduce Artificial Intelligence (AI), specifically a Machine learning (ML) algorithm, to inform PdM of railway infrastructure focusing on OLE and Track systems.
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Aseer et al. (2024) studied this question.
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