Predictive Maintenance
Drinking water pipe infrastructure for city development is often designed to last about 100 years. But as it ages, the associated challenges increase.
Reported impact
Since March 2024, South East Water has been actively working with SpiralData to explore the scalability of the TD•Cloud platform across a broader segment of the utility's pipeline network focusing on ‘hot' spots that have a history of leaks and breaks.
As demands on its infrastructure continued to grow, South East Water , a Melbourne metropolitan water corporation, established by the Victorian Government in Australia, sought an innovative approach to monitoring its pipe network. In collaboration with data analytics agency SpiralData , an AWS Partner , they trialed the Artificial Intelligence (AI)-powered TD•Cloud . Built on Amazon Web Services (AWS) , the platform monitors and analyses fast-moving pressure fluctuations that cause sustained pipe fatigue across focus areas of the utility's 27,000km pipeline network. This strategic initiative not only enhanced the water corporation's operational efficiency but also strengthened the resilience of water services for over 1.8 million residents in Melbourne's south-east.
With TD•Cloud, the water utility can monitor pressure transients with >97.5 percent detection accuracy. Built on AWS, the TD•Cloud solution features a real-time dashboard for monitoring and analyzing pressure transient patterns. This streamlines decision-making across various management and operational teams by serving as a single source of truth.
Built on Amazon Web Services (AWS) , the platform monitors and analyses fast-moving pressure fluctuations that cause sustained pipe fatigue across focus areas of the utility's 27,000km pipeline network.
But as it ages, the associated challenges increase.
In collaboration with data analytics agency SpiralData , an AWS Partner , they trialed the Artificial Intelligence (AI)-powered TD•Cloud .
These are abnormal fluctuations in water pressure that stress the pipe network, causing microtears that reduce pipe durability.
This innovative system monitors transients in near real time from high-speed pressure sensors and uses AWS-hosted machine learning (ML) to deepen its analysis and improve the accuracy of its predictive maintenance strategies.
This strategic initiative not only enhanced the water corporation's operational efficiency but also strengthened the resilience of water services for over 1.8 million residents in Melbourne's south-east.
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