An Australian-Indonesian joint research venture is uncovering an extra layer of complexity that influences the structural integrity of railway networks and the rolling stock that runs on them.
Maintenance of both tracks and vehicles is clearly of vital import for reasons of safety and efficiency, but operators need to strike a constant balance between the downtime needed for repairs and the economic obligation to keep the system running.
Finding the sweet spot requires not only monitoring the physical state of the rails, engines and carriages, but also a detailed understanding of how changes in any one of these affects the others. Railways are hugely dynamical systems, but understanding the interaction between moving wagons and the tracks on which they move has proved challenging.
Now, research conducted by the Institute of Railway Technology (IRT), housed at Australia’s Monash University, the Australia-Indonesia Centre’s Infrastructure Cluster, the PT Kereta Api Indonesia national rail company and other organisations aims to provide a more detailed picture, allowing better maintenance scheduling.
“For railways, it’s standard practice to measure the conditions of the track periodically,” says IRT research engineer Nithurshan Nadarajah.
“However, the influence of a track’s condition on the vehicle isn’t fully understood. So, the thresholds for when to intervene with maintenance aren’t comprehensive, or optimised.”
Using data collected by a specially built railcar, Nadarajah and colleagues are refining an algorithm designed to not only predict detailed maintenance timetables but also to identify the optimum running speeds for different types of vehicles on different sections of track.
The railcar houses IRT-developed equipment called Instrumented Revenue Vehicle Technology (IRV). To gather its baseline data, it was set up in Indonesia in 2016 and driven back and forth for several weeks along a stretch of track between Surabaya and Lamongan in East Java.
The information received was added to earlier data gathered from using the railcar along Australian tracks.
“Lots of relevant data is helping our computer algorithm learn about the relationship between track conditions, running speeds, and the response of a moving train under these conditions, Nadarajah explains.
“This work will help operators predict the response of different wagons, and identify maintenance requirements based on performance.”
Indonesia’s railway network is growing fast, bringing with it an increase in the number of trains running along it – and the number of expensive, time-consuming and hazardous derailments.
Nadarajah says the present system is hampered by “the crippling limitations of traditional passive assessment”.
The IRT’s algorithm, the researchers hope, will improve efficiency – and save lives.