Using the internet of things to monitor low adhesion on railways

Abstract

Adhesion refers to the ‘slipperiness’ of the rails due to surface contaminants such as leaves, rust, oil and grease, exacerbated by small amounts of atmospheric moisture from drizzle, dew or fog. Low adhesion is an issue on the railways because it reduces acceleration and braking efficiency. This leads to platform overruns and signals passed at danger, putting the travelling public at risk, as well as contributing significantly to service delays. In response, high-resolution forecasting systems have been developed that take into account site-specific leaf-fall forecasts, rail and dew-point temperature to estimate the occurrence of dew, frost, light rain and fog. However, in order to validate models, data are required from a high-resolution monitoring network that is able to capture observations of rail moisture and leaf-fall contamination. This paper investigates the feasibility of harnessing the emerging internet of things to develop a high-resolution, but low-cost, rail moisture monitoring network. A low-cost, self-contained sensor was developed and tested, with positive results, against existing, more expensive sensors in both a laboratory and field setting. The paper concludes with a blueprint documenting an approach to improve the spatial resolution of moisture measurements across the network.