By identifying bad actor locomotives and wagons, we can extend wheelset service life, minimise downtime, shunting, and wheelset changes. This improves our availability and reliability while reducing unit costs.

Bruce Brymer, Manager Reliability Engineering and Train Systems, Pacific National
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Data science identifying fleet wheel wear characteristics

Artificial Intelligence and machine learning improves safety and service through predictive maintenance


Pacific National operates a connected network, moving essential goods across Australia. As the country’s largest private rail freight company, Pacific National states that it is integral in keeping the economy moving. With a rich history dating back to 1855, it is proud of its heritage and the essential role it continues to play in supporting Australia’s supply chain.


Pacific National wanted to enhance the value of its data from high-speed cameras that monitor the condition of its wheelsets. It needed a data analysis platform to assess the data generated and provide new insights.


Pacific National engaged Versor to create an AI-enabled solution to assess the data generated. The new solution is based on Databricks and Azure, which analyses millions of data points over 12 months, calculates wear rates and identifies wheelset wear rates for a range of key parameters.


  • Faster fault wear rate calculations, quickly identifying the bad actors and good performers in the fleet
  • Improved decision making by isolating changes in performance
  • Improved safety and service through condition monitoring and action

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