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Artificial brains installed on NSW trains

Artificial brains installed on NSW trains

Data analytics platform on steroids for Waratah trains

A fleet of Sydney’s Waratah trains have been fitted with intelligent software created by Downer EDI and Microsoft Azure teams to keep track of vehicle maintenance and other data-based decisions.

The NSW government ordered 24 Waratah Series 2 trains under its Sydney Growth Trains Project in 2016 - an additional 17 trains was added earlier this year and another 24 will be delivered this month. The trains are maintained by Downer EDI – which holds a 30-year contract with the state government to manage and maintain the existing fleet of 78 trains.

Recently, Downer equipped the Waratah fleet of trains with over 300 sensors and about 90 cameras, backed by software that consumes sensor data from the fleet of trains. This allows predictive maintenance and data-driven decisions, said Mike Ayling, general manager of digital technology and innovation at Downer.

According to Ayling, its engineers will be able to analyse trends across very granular data such as temperature of the train, outliers in voltages and currents and opening and closing times of doors. This means any small changes in the data can deliver Downer an early alert about what’s going on and what might need attention.

Machine learning and intelligent data analysis could also allow engineers schedule preventive maintenance before a failure takes place; and help in the need for replacement parts, to be ordered ahead of schedule, from overseas suppliers.

Downer’s rollingstock services business is one of the first adopters of the Azure based solution, used as the backend for their TrainDNA product.

 Tim Young, executive general manager - Rollingstock Services, Transport and Infrastructure, Downer likened the solution to, “data analytics platform on steroids."

“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,” he said.

 “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.”

 The front end of the solution is an Angular Web App built on top of ASP.net core services, with the solution hosted through Azure’s Service Fabric ensuring scale and resilience.

 The Azure IoT Hub feeds stream analytics into an Azure Data Lake Store and Azure SQL database and access is managed by Azure Active Directory with Power BI providing analytics and reporting. 

According to Ayling “automation and digitisation” moves the company away from being an “inspector and maintainer”, to an asset maintainer.

 

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Tags big dataAItrainssensorsIoT

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