John May, Digital Services Business Development Manager at Knorr-Bremse Rail Systems UK, said:
“Data collected through our railhead low adhesion monitoring system has the potential to provide rail industry customers with a much clearer understanding of not only when low adhesion has occurred, but also where it is happening and what action may be needed to address it.
“The success of the first year of the trial has demonstrated the value of real-time adhesion monitoring. The quality of the data collected means we can identify low adhesion hotspots and seasonal trends with a high degree of accuracy. Extending the trial will strengthen the dataset further and help demonstrate how this intelligence can support a more proactive approach to managing low adhesion.
“As the trial enters its second year, our team believes the technology has the potential to transform how low adhesion is understood and managed across the UK rail network, enabling smarter interventions, more targeted railhead treatment and improved operational performance during challenging seasonal conditions.”
Unlike conventional systems that indicate only when wheel slide or traction slip has occurred, the Knorr-Bremse system calculates wheel-rail adhesion values during braking events, notably in the moments before and after a WSP activation; an invaluable insight that the industry did not previously have. This enables engineers to assess the actual condition of the railhead and understand how slippery it is in operational service.
“Individual parameters can be selected as required to obtain the required information. Adhesion hotspots have been identified in areas that were not previously considered by Chiltern and Network Rail and a lot of progress has been made in better understanding the kinematics behind low adhesion events – in particular in the milliseconds in the lead up to and immediately following a WSP activation.“This is very much in line with the main objective of this initiative; to develop a model which acts as the industry’s primary means of predicting low adhesion risk at any location and given time throughout the year. The next challenge will be to build on this capability and enable the WSP system to proactively utilise this data to directly control the train’s response to low adhesion, thereby reducing dependence on infrastructure mitigation measures or driver behaviour.“There is now a strong case to scale up this trial to include additional units both at Chiltern and across other networks. This would help demonstrate the full potential of this concept, both in terms of predicting low adhesion risk to aid operators and infrastructure managers in their decision making as well in providing further technical insight for incident investigations and informing delay attribution.”