The global water sector faces a massive operational challenge: getting ahead of the asset failure curve. Traditional predictive maintenance relies heavily on classic, purely data-driven machine learning models. However, when field data is sparse, low-quality, or missing altogether, these traditional models break down—forcing utilities into a continuously reactive state or requiring millions in expensive field-sensor deployments.

In this video, we explore how Binnies, a leading water engineering services provider, is driving a structural step change in utility operations. By looking beyond the water sector, Binnies formed an exclusive three-way partnership with elite motorsport engineering firm Williams Grand Prix Technologies and AI pioneer JuliaHub to bring Scientific Machine Learning (SciML) to the water industry.

Discover how deploying SciML on the Dyad platform allows utilities to combine fundamental physics-based modeling with traditional machine learning. This hybrid approach enables water companies to accurately predict subcomponent failures (like pump bearings) to greater than 95% accuracy and assess system-wide infrastructure health in real time—using only the data they already have, without installing a single new field sensor.