Our event in February was a double lecture presented by 2 Senior Lecturers from the University of Derby.
Dr. Uchenna Diala presented on the topic of Energy Harvesting within the rail environment and Dr Shadi Fathi discussed the developments in the field of Intelligent Rail Infrastructure Monitoring.
A video of this lecture is available to view by our Members. Please login and then navigate to Lecture Videos.
Energy Harvesting
Energy harvesting technologies are gaining increasing attention in rail applications as the sector seeks to improve energy efficiency, reduce maintenance costs, and support sustainable transport systems. Rail environments offer multiple untapped energy sources, including mechanical vibrations from passing trains, thermal gradients in braking systems, solar radiation along open track and stations, acoustic (noise) energy and ambient electromagnetic and radio-frequency energy generated by signalling and communication systems. By converting these forms of wasted or ambient energy into usable electrical power, energy harvesting enables the deployment of self-powered sensors and monitoring devices without reliance on batteries or wired power supplies.
In rail applications, energy harvesting is particularly valuable for condition monitoring of tracks, rolling stock, and infrastructure assets, where large networks and remote locations make conventional power delivery costly and impractical. Overall, energy harvesting supports the transition toward smarter, more autonomous, and sustainable rail networks by enabling continuous monitoring, improved safety, and reduced lifecycle costs.
Extensive studies on Vibration and Acoustic energy harvesting in rail have been explored at the University of Derby, and some of our works and publications will be presented.
Intelligent Rail Infrastructure Monitoring
Due to the very high costs of rail infrastructure maintenance, non-destructive assessment methods, such as the Falling Weight Deflectometer, are increasingly important for identifying early-stage degradation, enabling targeted maintenance, reducing unnecessary renewals, and minimising service disruption. When coupled with data-driven techniques, these methods support a transition from reactive monitoring to predictive asset management. Artificial Neural Networks have proven effective in modelling complex, non-linear relationships between measured responses and underlying condition. Recent developments in low-power sensing and energy harvesting further support long-term, self-powered monitoring solutions, enabling scalable, intelligent, and cost-effective railway infrastructure management.
