The University of Liverpool and PSW Integrity Limited KTP 24_25 R4

Offshore oil rigs, nuclear plants, and wind turbines will get a new kind of maintenance system that predicts failures before they happen, rather than waiting for something to break. Current industry practice relies on periodic inspections and reactive repairs. This leaves critical infrastructure vulnerable to undetected corrosion, fatigue, or structural damage that can lead to catastrophic failures. The project combines existing sensor technologies from PSW Integrity Limited with machine learning algorithms to create a "consequence-based engineering" approach. Instead of monitoring everything equally, the system prioritises components whose failure would cause the most harm. If successful, the system could transform how we maintain the infrastructure that keeps energy grids running and industrial plants safe. For offshore wind farms, it could reduce costly emergency repairs and extend operational lifetimes. For nuclear facilities, it could provide continuous structural health monitoring that goes well beyond current regulatory protocols. The sensors and algorithms will be optimised specifically for harsh environments—saltwater, radiation, high winds—where traditional inspection is difficult or dangerous. This is applied engineering research with a clear commercial pathway, not fundamental science.