This project aimed to improve the safety and efficiency of rail infrastructure in both Australia and Indonesia. Computer models to predict how railcars would respond to different track conditions were developed, allowing quicker identification of problems and improved maintenance schedules. The project was conducted with the support of the Government of East Java, the Institute of Railway Technology at Monash University, PT Kereta Api Indonesia (the national rail company), Java Integrated Industrial and Port Estate, the Lamong Bay Terminal container port, the Australian Rail Track Corporation, Public Transport Victoria and Monash University.
Railway networks offer significant advantages for transporting both passengers and freight as they occupy two to three times less land per passenger than other modes of road transport. Determining the structural health of track and rail vehicles is crucial to support this growth and ensure the safe and reliable operation of railways. Detrimental track conditions include heavy wear, corrugation, defective welds, formation failures and mud holes.
For railway operators, it is standard practice to periodically measure track conditions. However, the influence of track conditions on a vehicle isn’t fully understood, meaning thresholds for maintenance are neither comprehensive nor optimised. When different types of vehicles use the tracks, for example passenger and freight wagons, the risks associated with passive threshold-based maintenance increase. The increased demand on railways, particularly in a growing country like Indonesia, has exposed the crippling limitations of existing assessment methods, and a number of derailments have resulted from a combination of detrimental track features.
The project discussed the use of instrumented revenue vehicles (IRV) to evaluate track conditions and assess the performance of rolling stock during peak operating hours in an Indonesian passenger network between Lamongan and Surabaya. Assessment of track condition and vehicle response is crucial in setting safe operating speeds, developing economical, proactive maintenance plans and maximising throughput.
Track conditions and vehicle responses were measured over multiple journeys using various sensors strategically mounted on the in-service vehicle. The data were analysed and used to develop visual hotspot maps of the track to determine regions of high dynamic response. A web-based reporting system was used to provide plots and figures to communicate the results. Results presented demonstrate the ability of the IRV system to assess the health of track, identify regions of degradation and quantify the severity of the dynamic response. The project created a successful model for passenger carriages, validated against the performance of trains in Indonesia. The project also worked on models for freight trains.
It is important to acknowledge the challenges in determining track degradation due to the complex nature of dynamic loading and variations in network utilisation. The traditional solution in the industry has been to use dedicated Track Geometry Cars (TGC). These dedicated monitoring vehicles are equipped with laser measurement systems, accelerometers, gyroscopes and other sophisticated sensors that allow them to identify and quantify a range of track issues such as alignment, curvature, rail profiles and twist. However, a key drawback in a dedicated TGC is that they are often very expensive, and they provide no direct indication of the dynamic behavior of the typical revenue vehicles. An alternative to the dedicated cars is the use of wayside monitoring devices. However, these devices only evaluate the integrity of the rolling stock performance and track quality over a localised region of a track, making them an unfavorable choice to monitor significant sections of a network.
An alternative is the use of IRVs. This technology has helped substantially reduce operational costs and increase network safety. IRVs are a fully automated measurement platform that can be embedded on any standard in-service vehicle. They reduce the need for track down time and have the key advantage of allowing the response of in-service rolling stock to be measured under typical operational loads, thus providing information about track condition. It has been recognised a vehicle’s response to a given track feature is often dependent on dynamic characteristics of the individual wagon; armed with this information, plans can then be devised using the deterioration rates inferred from the dynamic rolling stock responses.
The project highlighted the capability of retrofitted vehicles to automatically monitor the track condition and vehicle responses without impacting on network operations. Mathematical modelling reinforced the capability of the IRV system to both measure and predict track deterioration rates. The use of data visualisation tools such as heat maps to summarise the condition of the entire track can help maintenance planners and railway operators better prioritise scheduled track maintenance.
Professor Wing Kong Chiu
Dr Hera Widyastuti
Head of the Transport Laboratory
Institut Teknologi Sepuluh Nopember
Dr Massoud Sofi
Research Fellow, Department of Infrastructure Engineering
The University of Melbourne
Dr Siva Naidoo Lingamanaik
Senior Research Engineer, Monash Institute of Railway Technology
Director, Institute of Railway Technology
Lingamanaik, S. N., Thompson C., Nadarajah N., Ravitharan R., Widyastuti H., & Chiu W. K. (2017). Using instrumented revenue vehicles to inspect track integrity and rolling stock performance in a passenger network during peak times. Procedia Engineering, 188, 424-431. https://doi.org/10.1016/j.proeng.2017.04.504
Chong, T. L., Awad, M., Nadarajah N., Chiu W. K., Lingamanaik, S. N., Hardie, G., Ravitharan, R., & Widyastuti, H. (2017). Defining rail track input conditions using an instrumented revenue vehicle. Procedia Engineering, 188, 479-485.