Predictive Maintenance and AI
Read the full article to see how predictive maintenance is being deployed across high-performance sectors.
Published
14 NOV 2025
Est. reading time
2 min
Predictive maintenance is about understanding how a component is degrading and estimating how much useful life it has left. There are two main ways to do this. One is to collect large datasets and train algorithms to detect early signs of failure. The other is to use physics-informed or Physics Machine Learning, which blends physical models with data to identify parameters that indicate wear long before it becomes visible.
Take a motor or a pump as an example. As the bearing begins to wear, friction increases. If there is no degradation or changes to the system, the friction coefficient will remain constant. If a model can detect that this value is rising, it can link that change to component degradation. With that knowledge, engineers can decide exactly when to carry out maintenance or replacement before failure occurs.
This is not just about observing faults, but about recognising patterns of change in component behaviour. The earlier these changes are spotted, the more time engineers have to intervene without unplanned disruption.
Beyond simply detecting wear, predictive maintenance allows forward projection. By analysing how quickly the friction coefficient is changing, the system can estimate when the component will fail. This means maintenance can be planned based on real evidence rather than on fixed schedules or reactive repairs.
This allows operators to prioritise repairs by actual condition, not assumptions. It also provides the ability to balance maintenance windows around demand cycles, reducing operational impact.
Moving away from reactive or overly cautious preventative maintenance towards a data-driven, condition-based approach reduces downtime, saves costs and improves operational efficiency. Whether in aerospace, energy, manufacturing or transport, predictive maintenance ensures assets stay in service for as long as possible while avoiding costly unplanned failures.
If you have a specific question or business enquiry, please contact us here.