Articles & Case Studies

Yorkshire Water sees promising results for AI leak detection trial

Posted: Friday 13th August 2021

Yorkshire Water has deployed artificial intelligence (AI) in a trial hoping to detect leaks more effectively. 

Delivered in collaboration with Siemens and Artesia Consulting, the project has shown that through using trained analytics packages, the number of false alarms has the potential to be reduced by up to 60% and the system can simultaneously identify more true leaks.

Nearly 40,000 acoustic loggers are deployed across Yorkshire Water’s clean water distribution network. Each morning the loggers listen for evidence of leaks on the network of pipes. If a leak is suspected, an analyst confirms and an inspector goes to site to investigate the alarm, locate the issue and raise a repair. Sometimes, the alarms from loggers are found to be false positives, where background noise has been mistaken for a leak.

Using advanced machine learning techniques and analysis of audio recordings, the project looks to improve the alarm process by teaching the AI engine to distinguish between a leak and background noise.

Sam Bright, innovation project manager at Yorkshire Water, said: “Reducing leakage is a key priority for us and we know that adopting improvements in technology, such as this, are key to hitting our ambitious leakage targets.

“So far, we’ve seen some promising results from Siemens and Artesia Consulting – the project, if successful, will help us to reduce leakage by allowing us to target our activities more effectively.”

Adam Cartwright, head of IoT application delivery at Siemens, said: “Acoustic data is complex and challenging. Only by combining Siemens data science experts from multiple sectors, like oil and gas, with Yorkshire Water’s deep knowledge of water networks could we train such a powerful AI.”

Sarah Rogerson, data strategist at Artesia, commented: “At Artesia, we love tricky problems. This opportunity challenged both our data science and industry experts quite nicely! We were pleased to be able to improve the accuracy and sensitivity of this technology."

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August 2021

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