Binnies hails a breakthrough for asset health as it pioneers AI advancement with F1

Binnies has teamed up with Formula 1 and technology pioneers to introduce a new class of AI to the water sector, to overhaul asset health.

The collaboration – between engineering expert Binnes, F1 consultancy Williams Grand Prix Technologies and AI specialist JuliaHub – deploys scientific machine learning (SciML). This combines physics with machine learning, using scientific laws – such as fluid dynamics and thermodynamics – to fill data gaps and model how assets should behave. This supports accurate predictions, even without historical data or with only minimal sensors.

The ambition is for this new AI class to overcome the problems water has faced with traditional machine learning, given asset and performance data in the industry is often missing or of poor quality.

The approach is proven in top-level motorsport, aviation and aerospace, where companies are extracting predictive insights largely from the data they already hold, thereby reducing the need for costly sensor deployments or large-scale data cleansing.

Binnies said the impact on water could be groundbreaking, and will support water companies to shift from reactive to predictive asset health to prevent failures. Tom Ray, director of digital products and services at Binnies UK, said: “This partnership could fundamentally change how the water industry approaches asset health. For the first time, predictive insight doesn’t require perfect data, and that’s a breakthrough the industry’s been waiting for.”

The team added: “This marks more than just a tech upgrade – it’s a mindset shift. The sector is moving from reactive to predictive operations, from lagging indicators to leading insights. By rethinking how existing data is used, risk is managed, and investment is targeted, this partnership unlocks a new level of operational resilience across the industry.”

Early projects with Southern Water and Anglian Water have begun to validate the technology in water, and have already successfully shown that SciML can help unlock predictive asset health at pace and scale.

Binnies highlighted that the approach supports the Cunliffe Review’s call for asset health reform, and will help water companies get ahead of the failure curve, even with limited data.