How to identify high-speed pressure anomalies in a water network to reliably investigate, mitigate and ultimately, extend asset life.
The complexity of a water pipe network can be compared to that of arteries, veins and capillaries in the human body. In recent years there has been increasing effort within the water industry to detect water main leaks or breaks before they can impact the community, the equivalent to a stent. However, there has been very little applied research into identifying pressure anomalies within water networks at a high frequency, the equivalent to healthy lifestyle choices that act as preventative protection against severe outcomes.
Anomalies in pressure are commonly known as transients, pressure waves or water hammer. Slow transients can occur over longer time horizons (usually minutes or hours) and are assumed to have the least detrimental impact on a water network. Whereas fast repeated transients are sudden changes within the pipe1 resulting in potential breaks in weakened sections, which must be detected over a time scale from milliseconds to seconds.2
Over the past several years, SA Water, as part of their smart network project, has collected a big data set from high speed (128 Hz)pressure sensors located within their network. While there has been research on detection and characterisation of transients, the scale of big data SA Water has collected at consistent monitoring locations represents a unique opportunity.
Pressure transient monitoring
Previous work by SA Water and the University of Adelaide uncovered the prevalence of moderate-sized transients being far more frequent than previously thought3, with many potential causes. The existing SA Water system used to analyse the high-speed pressure data was originally intended for main break detection, however it also provides an opportunity to analyse the prevalence, magnitude or shape of transient events, and therefore, the capability to triage the root-cause of transients.
A new approach was required. It needed to solve the big data and algorithm challenges to reliably detect fast transients that occurred frequently, so that they could be investigated and resolved.
Example of pressure transient events followed by a main burst.