A Review of Condition Monitoring, Fault Detection, Diagnostic, and Prognostic Approaches for Electropneumatic Point Machines

Electropneumatic point machines (EPMs) play a critical role in railway signalling systems by enabling the safe routing of trains at junctions and are widely deployed across the London Underground (LU) network. Despite their operational importance, many EPMs on the LU are maintained using periodic inspection regimes, and the literature addressing their condition monitoring and health management remains fragmented. Although fault detection and diagnostic approaches for point machines have been widely studied, existing research predominantly focuses on electromechanical and electrohydraulic systems, with limited consolidation of findings specific to electropneumatic points. This paper presents a structured review of fault detection, diagnostic, condition monitoring, and prognostic approaches relevant to EPM. The review synthesises reported findings from the literature, examining failure mechanisms, monitored parameters, analytical techniques, and their respective strengths and limitations. Reactive and preventive diagnostic methods are compared for their ability to identify abnormal behaviour and incipient degradation, while prognostic approaches are reviewed for their potential to support maintenance planning. Based on insights from the review, a remote condition monitoring framework is proposed that integrates asset prioritization, multi-parameter sensing, diagnostic functions, and prognostic assessment into a coherent monitoring pipeline. The framework emphasises phased deployment, functional parameter selection, and cautious interpretation of analytical outputs. It provides practical guidance for industry practitioners considering the implementation of condition monitoring for EPMs on the LU. The paper concludes by outlining key research gaps and future directions to support the transition towards more condition-based, data-informed maintenance strategies.

Keywords: Electropneumatic Point Machines, Remote Condition Monitoring, Fault Detection and Diagnosis, Predictive Maintenance, Asset Health Management.