Using Internet of Things technology to inform pre-emptive maintenance and improve the customer experience

Relevant RTS critical priority(ies):
Reliable and easy to maintain; Data driven

Research by Transport Focus, Rail Delivery Group, the Office of Rail Regulation and others has shown a trend from customers focussing their concerns, and therefore their complaints, on the issues of punctuality and the experience of the journey itself.

Although punctuality is the single most important factor in customer complaints, experiential factors are nearly on the same level. If customers are advised about the status of their journey, the train and its facilities, they can plan accordingly and are less likely to suffer stress and to complain.

Internet of Things (IoT) sensor technology is being used to monitor saloon air temperature, pressure and humidity. Inexpensive, battery-powered IoT devices have been installed in Grand Central carriage interiors to help the operator identify potential maintenance issues and improve the customer experience. The trial of the technology, led by Pure Chaos and delivered by Oodl, ran during Summer 2021.

The sensors use 5G spectrum connectivity to transmit live data to a central hub. Grand Central can view a dashboard of real-time information about conditions on board carriages, and receives alerts when readings stray outside the expected norms.

The dashboard, which displays the data downloaded from each train in real time, enabled maintenance technicians to identify air conditioning defects and arrange corrective action at the earliest opportunity.

The data that is collected has also been used to train machine learning algorithms to predict heating, ventilation and air conditioning outages, with the aim of informing pre-emptive maintenance planning to reduce the likelihood of disruption caused by trains being taken out of service.

For more information see Using 5G spectrum connectivity and Internet of Things sensor technology to improve the customer experience (COF-G23-01) (rssb.co.uk)