Case Study

Aggreko: Improving fleet reliability through data insight

Posted on December 05, 2017

Glasgow-based Aggreko is the leading global provider of modular, mobile power, heating and cooling to industries including events, mining, oil and gas, utilities, petrochemical and refining, construction and manufacturing, as well as food and beverage.

Aggreko is Scotland’s 10th largest company, employing around 7,300 people globally and working from 200 service centres and offices in more than 60 countries. Its business ranges from large power projects supporting utility companies and government infrastructure in developing countries to shorter-term rental projects wherever needed, as well as emergency provision of power and heating and cooling.

Project detail:

Aggreko wanted to monitor and be able to predict and react to any potential generator maintenance issues to ensure it continues to deliver uninterrupted and reliable power to its global customers who depend on it to thrive. Essentially, the development team wanted to be able to predict equipment issues by analysing historical data and comparing it with current symptoms, to maintain the reliability of equipment and improve customer service.

To do so, Aggreko approached The Data Lab, who in turn partnered Aggreko with the University of Strathclyde to deliver academic insight and data science support using the latest machine-learning techniques to enhance the Aggreko Remote Monitoring system (ARM).

The solution:

Telemetry technology (ARM) is fitted to Aggreko’s rental generator fleet, to collect control data from the generators and stream information to its Global Technology Centre in Glasgow. This information is then fed to the Remote Operations Centre (ROC), in Louisiana, North America, where any alerts and alarms being raised by the generators are monitored by experienced technicians. They can then take proactive action to improve reliability and deliver enhanced customer service.

“Our partnership with Data Lab and University of Strathclyde has transformed our capability as an organisation. Machine Learning and Advanced Analytics have helped us improve reliability, prevent equipment damage and improve customer service. It’s been an invaluable project, and we are very grateful for the support.” - Steven Faull, Head of Software and Analytics, Aggreko

Technical:

Telemetry control points from the generators are streamed to the Aggreko Data Centre. SQL Server Integration Services extract the change dataset from the Data Centre to a SQL Azure database allowing for offline interrogation of the data.

The data is then analysed in the statistical computing and graphics package R and then transferred to the Azure machine learning studio, where Machine Learning models are developed.

The Data Lab has been instrumental in shaping and building Aggreko’s advanced analytical capability. I approached The Data Lab a couple of years ago when looking to expand Aggreko’s data science expertise. Since then, our partnership has enabled the successful completion of the generator maintenance project, allowing Aggreko to predict equipment issues before they occur. We have a great working relationship with The Data Lab where the Aggreko Software and Analytics team regularly take part in their first-class education programmes.


The Data Lab’s commitment to building the data community and pipeline of talent in Scotland has helped many businesses realise the potential of data. They have been instrumental in advancing Scotland’s data science capabilities. I’m a big advocate of The Data Lab; without their help we would not be where we are today in our data analytics journey.

- Steven Faull, Head of Software and Analytics, Aggreko

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