Monitoring and maintaining the infrastructure of Australian water utilities is an expensive and ongoing commitment, particularly as often assets are ageing and deteriorating. The distance between assets in these large networks also adds to the cost of maintenance.

Advances in machine learning algorithms are enabling Siemens to work with water and wastewater utilities to help reduce maintenance expenditure through the early detection and identification of potential failures in water and wastewater pumping stations.

At its Australian headquarters in Melbourne, Siemens engineers have developed SIDRIVE IQ Snap Advanced Health Analytics, a vendor-agnostic solution that uses data already gathered by water utilities, overlayed with the Siemens algorithm.

“Through the early detection of abnormal conditions in water and wastewater pump station drive trains, pump station operators have greater transparency over their assets and more control over their maintenance planning,” Dr Liam Pettigrew, a Senior Data Scientist at Siemens Australia, said.

“The algorithm analyses long-term trends in existing SCADA data to see how a particular pump has been operating over time and how current operations compare, providing real-time updates on the condition of the asset.

“When an irregularity is detected, it sends an alert to the operations team informing them to send maintenance teams to investigate. Predictions of a higher failure risk on pump drive trains in both water and wastewater pumping stations occur from one day to over 14 days in advance of actual breakdowns occurring.”

Making data-driven decisions

Dr Pettigrew said undertaking a digital overhaul of a vast number of assets and pumps can be daunting for utilities, and recommends starting with the existing data from their SCADA and asset management systems to see what value can already be added before installing any new hardware and sensors.

Through SIDRIVE IQ Snap, Siemens clients have the ability to predict the estimated time until event failure which is provided through a live dashboard, automated failure notification and automated monthly reports.

Critical information is continuously pushed from the pump station drive train and aggregated in a secure web portal for around-the-clock visibility of pump drive train operation and status from any location.

This continuous monitoring and feedback provides the necessary information to make data-driven decisions that move the industry towards its goal of a full prescriptive analytics solution.

“Our clients are finding true value in the SIDRIVE IQ Snap Advanced Analytics Platform. The ability to detect abnormal operation and predict failure/imminent failure of pump stations drive trains has allowed for better maintenance planning.

“This has reduced OPEX costs associated with maintenance and inventory whilst also improving safety through the reduction of required site visits and emergency breakdown repairs. Siemens customers are seeing positive improvements in plant standstill versus uptime,” Dr Pettigrew said.

This is a Sponsored Editorial brought to you by Siemens. For more information, please visit

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