Intangles: how predictive maintenance drives sustainability
Fleet management solutions provider Intangles has outlined how its predictive maintenance technology can help improve fleet operators’ environmental footprints.
“By integrating Digital Twins and predictive maintenance, Intangles is helping fleets evolve from reactive, resource-heavy operations into streamlined powerhouses—ultimately saving time, cost, and carbon,” said the company.
A Digital Twin is a dynamic, virtual representation of a physical asset, fuelled by real-time data drawn from the vehicle’s CAN.
“Because these models operate with 95 per cent accuracy of predictions, managers can trust digital data to make decisions that directly impact their environmental footprint. This visibility allows companies to simulate ‘what-if’ scenarios, optimising routes to reduce fuel burn without risking real-world assets.
“While many original equipment manufacturers (OEMs) provide fuel data, standard telematics often lack the granularity needed for true optimisation. Intangles provides high-precision monitoring, transforming fuel management from an estimate into an exact science.
“By analysing high-frequency fuel system signals such as fuel rail pressure and comparing them to a learned model of normal engine behaviour, the system detects subtle discrepancies that traditional OEM systems miss, providing highly precise fuel measurement and early visibility of emerging inefficiencies.
“This typically results in a 2–10 per cent reduction in fuel use, ensuring every opportunity for saving time, cost, and carbon is seized.”
Intangles says it can also help operators navigate the shift to zero emission vehicles (ZEVs).
“By providing a clear health and efficiency profile for every internal combustion engine (ICE) vehicle, the technology identifies which assets are the least efficient or most prone to failure. This data makes it easier to decide which vehicles to swap out first for expensive ZEV replacements. Instead of a ‘best guess’ approach, managers can prioritise renewals based on actual performance data, ensuring the highest return on both investment and carbon reduction.”
