This presentation contains an analysis of autumn performance data from 2015. The graphs provide a comparison of autumn performance in 2015 to 5 year average. The slides contain a comparison of leaf fall percentages, safety KPIs (such as wrong side track circuit failures and SPADs) and delay minutes.
On behalf of the Adhesion Research Group, this research has combined the directly applicable railway adhesion domain knowledge and experience of industry stakeholders with a review of the existing evidence base.
This research has developed a new technique for detecting low adhesion conditions by monitoring running conditions on-board a vehicle. The method is a predictive technique based upon measuring what is happening dynamically to the wheelsets and bogie during normal running, prior to braking.
The overall aim of this project, funded by RSSB, was to prove the feasibility of harnessing the emerging Internet of Things (IoT) to develop a high resolution, low cost moisture sensing monitoring network for the railways.
Document describing Phase 2 findings from WILAC research. The objectives of Phase 2 were to develop a creep force model that is able to account for the effects of water in the wheel/rail contact, to determine model inputs from experiments and to carry out validation tests.
This report describes the current version of the LABRADOR simulation tool that can predict the train brake system performance and support decision-making in the design and optimisation of the braking system including WSP, sanders and the blending and control of friction and dynamic brakes.