Artificial Intelligence and advanced data analytics are used for anomaly detection and sustainable delivery of utility services in Australia.
The water sector has faced many global challenges in the past centuries, from plagues and population growth to recurring water shortages and more recent climate change concerns. Although droughts, floods and bushfires are not new occurrences in Australia, we constantly need new solutions that are able to address current and future challenges in utility management.
Technology facilitated by Industry 4.0 is playing an important role to confront these issues and better manage utility networks. The use of advanced data analytics and machine learning is transforming the way the water sector plans and maintains its assets, to ensure liveable communities and resilient water supply.
Using statistical tests and deep learning prediction algorithms, SpiralData and SAGE Automation have developed an automated solution that can detect abnormalities (such as blockages, also called chokes) within a sophisticated water network across varied geographical locations.
“We wanted to serve our customers better. Water utilities typically have SCADA, a system that still effectively serves the purpose of monitoring and control. Data analytics has the ability to compliment SCADA, with keener insights into patterns of asset behaviour to identify choke triggers which can save operation costs,” said Kale Needham, CEO of SpiralData.
The collaboration resulted in a low-cost, scalable anomaly detection platform using Amazon Web Services (AWS) that integrates with SCADA, PLCs or any device that can be visualised in real-time and send automated alerts for improved field operations and asset maintenance.
These new approaches to analyse, plan and manage water systems will help us build resilience to climate change and overcome some of the water security challenges. Being agile and able to respond to action needed is key for the industry moving forward.
SpiralData and SAGE Automation collaborate on IoT platform to automate detection of abnormalities across water utility networks.