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Improving safety through early track void detection

Dr Farouk Balouchi, project technical lead at the Institute of Railway Research, University of Huddersfield, Dr Adam Bevan, head of enterprise, Institute of Railway Research, University of Huddersfield, and Roy Formston, lead development engineer at Siemens UK, explain how a new vehicle cab-based monitoring system could help improve track void detection.

The GB railway is one of the busiest – consisting of 15,760 km – and densest networks in Europe. Currently track recording vehicles survey the network and detect track faults. These vehicles are deployed more frequently on mainline routes with higher traffic volumes, but less frequently on branch lines due to the limited availability of this resource. This has a potential impact on the safety and maintenance costs of these lines. 

An RSSB and Network Rail initiative called upon the railway industry to come up with potential solutions towards resolving these issues. Siemens partnered with the Institute of Railway Research (IRR) to develop an inexpensive Remote Condition Monitoring (RCM) system to monitor the track from in-service rail vehicles using the existing GSM-R cab radio system. 

The problem 

The support conditions of a track system can change significantly for a number of reasons, including contaminated ballast, drainage problems (wet beds) or gaps between the sleeper and ballast layer. If not monitored and maintained regularly, these can develop into voids underneath railway tracks which can cause substantial delay minutes and possibly line closures. 

Voids located within high-value assets, such as S&C, tend to have a feedback effect. A voided sleeper is not able to support the vehicle axle load and does not transfer any force to the ballast layer. Instead, the force is distributed on the adjacent sleepers around the void which causes the void to grow in size, resulting in greater track movement. Eventually, if unattended, such track movement causes damage to the S&C which then requires repair work to allow continued operation. The increased deflection of the rail also increases the risk of rail breaks, and therefore becomes a safety concern, along with resulting in poor vehicle ride performance (e.g. passenger comfort). 

Solution 

The Siemens Tracksure track monitoring system is able to identify voids underneath track from the acceleration response measured in the vehicle cab. It can also identify the type of track asset (e.g. S&C, structure or plain-line track) that the void is located under and provide an indication of the severity of the void. Installation of a Tracksure sensor card into multiple trains provides the potential for automated monitoring of a significant proportion of the rail network, including small branch lines, as well as the assessment of each track-section by multiple trains. 

Analysis and trending of the acceleration data by the Ground System takes advantage of the multiple passes over a track section that have been recorded by each train. This allows the identification and removal of false alarms, and improves the estimates of location accuracy and void sizing. As well as reporting voids, the Tracksure Ground System will also report the occurrence of large vertical or lateral accelerations at S&C and plain track, when observed by multiple trains, which could indicate the location of other defects. 

Project outcomes 

The RSSB-funded study has supported the development of a prototype Tracksure system which allows for in-service multi-train monitoring of the track, using existing technology, for the detection of track voids and other track defects.  

Through simulation, signal processing and analysis, the IRR has developed a detection algorithm using a state machine design methodology and tested it with experimental data. The test results show a good agreement with reported track faults at S&C, bridges and on plain-line locations of the test routes. 

The system provides a low-cost solution to identifying and monitoring track defects that could provide additional coverage of the GB rail network, and feeds directly into existing infrastructure maintenance tools. 

Increased reliability, lower maintenance costs, reduced delay minutes from line closures, and reduced speed restrictions can be seen as immediate benefits of the system. Furthermore, the inclusion of multi-train self-learning algorithms can be used to predict with more accuracy the condition of large portions of the network, supporting the requirement for more predictive maintenance.

FOR MORE INFORMATION

W: www.hud.ac.uk/research/researchcentres/irr

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