Understand critical points of inspection in railway assets to safety and efficiency of maintenance strategies of railway operations using automated technology for asset management. This presentation will be delivered in two parts.

Part 1: Automating Rail Network Asset Management (presented by Daniel Buntine)
Using automated inspection systems, Aurizon Network is on a journey to ramp up network inspections to meet the requirements of a modern heavy-haul railway. Aurizon Network operates Australia’s busiest coal transport system, and with such immense traffic comes rapid degradation of asset condition, quality and performance. Through targeted investment in emerging technology to link developments in asset measurement, telecommunications, data analysis and asset management, Aurizon is on track to monitor critical safety parameters much more frequently and facilitate a shift towards more predictive maintenance and renewals across the asset portfolio. Two case studies will focus on how the $30m annual rail replacement programme is now generated accurately at the push of a button, as well as new industry-leading technology being trialled to increase measurement frequency of track and overhead traction geometry by up to 100-fold.

Daniel Buntine, MIEAust CPEng, Asset Management Engineer, Aurizon

Daniel is an engineer and data guru in Aurizon’s Network Asset Management area. He is passionate about leveraging technology and crunching numbers to help make better business decisions that lead to improved operational performance for Aurezon’s customers. Daniel has previously completed investigations into extreme rail temperatures under traffic at Connors Range near Sarina, the relationship between curve superelevation imbalance and rail wear, as well as spearheading visualisation of asset information, activities and disruptions through GIS platforms.

Part 2: Inspections via Machine Vision and Why You Should Be Excited (presented by Nicholas Keogh)

The maintenance of a railway poses numerous unique engineering challenges, many of which are exacerbated by the physical environment in which the railway exists. A heavy haul railway can stretch over thousands of kilometres, often to remote locations, and is directly exposed to harsh physical conditions all year round. Exposure to such elements raises the risk of failure and necessitates that the condition of the railway is continuously monitored to ensure safe operation; this poses a significant logistical challenge to maintainers. How best to safely and efficiently provide maintainers with the inspections they need to manage the asset? In 2019, Aurizon embarked on a trial of using an inspection regime that relied on autonomous object detection within videos captured from track vehicles, with the aim of using machine learning to identify and report on basic defects.

This presentation will provide an overview to the problems being sought by the trial, the process followed throughout as well as what it was able to find.

Nicholas Keogh, Senior Track & Civil Engineer, Aurizon

Nicholas Keogh is a distinguished Senior Track & Civil Engineer who’s worked in various streams of the transport sector for over 10 years. He found his engineering passion while finishing his trade in aviation maintenance at Boeing before moving on to the Heavy Haul railway industry where he’s spent the last eight years with Aurizon. When he’s not wading through asset data and automating solutions to meet business needs, he’s looking into how we can use today’s technology to provide a safer future.

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