Australian rail operator Downer has taken a step towards predictive maintenance on its trains with the rollout of a new data analytics platform.
The data will come from the trains’ over 300 sensors and 90 cameras. Every 10 minutes, the company receives 30,000 signals from each train.
Downer’s Microsoft Azure-based TrainDNA platform will capture and store the Internet of Things (IoT) data, and use advanced data analytics and visualisation tools to make sense of the information. This allows Downer engineers to make data-driven decisions about train maintenance, according to Microsoft.
Thanks to machine learning and intelligent data analysis, Downer can even predict the likelihood of failures, sometimes months in advance, and schedule preventive maintenance before a failure occurs. It can also place early orders for replacement parts which may need to be shipped from overseas.
Lee Hickin, national technology officer at Microsoft Australia, commented: “Downer has taken a complex IoT data collection and with the support of Microsoft used cloud and artificial intelligence to extract real meaning from the data and get it into the hands of engineers so that they can schedule and carry out maintenance on time, every time.”
Tim Young, executive general manager for Downer’s Rollingstock Services, Transport and Infrastructure, added: “With such massive volumes of data it will allow us to establish trends in relative real time, allow us to predict failures in advance and calculate the remaining life of an asset more effectively.
“The advantage to our customers is that all of this takes place whilst the train is in service without interrupting the operation and at the same time enhances worker safety through the potential of removing high risk inspections. These enhancements in our asset management capability will boost our ability to better predict failure rates and reduce unscheduled downtimes of our train fleet.”
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