Design, Modelling and Simulation

Intelligent data fusion and analytics framework

Downer (Saad Khan, Mike Ayling, Loic Ayoul) / Deakin University (Doug Creighton)

Research summary

Downer has built a digital platform called TrainDNA, which seeks to digitise data taken from internal and external train monitoring systems. This platform uses cognitive computing tools to display up-to-date information on train location and status. Building upon this platform, the data science capability was augmented using Deakin University research methodologies to develop algorithms for predicting maintenance needs.

Start/end date

18 April 2019 to 22 March 2020

Total contracted budget (including in-kind)

$2,757,640

Key achievements

• The team developed algorithms to assess train components to predict more accurate maintenance regimes and identify potential failures prior to occurring.
• The data analytics work being conducted has been implemented into Downer’s TrainDNA system, enabling engineers real-time access to data analytics.
• The analytics framework enables new models to be built that will assist Downer and its customers reduce down-time and optimise rollingstock maintenance.

Publications

Chen, J., Jindal, H., Philpot, D., Khan, S., Lim, C. and Creighton, D. Can I Trust Machine Learning Analytics? Downer’s Approach to Predictive Maintenance, AusRAIL PLUS 2019, 3-5 December 2019, Sydney Australia.