Wind energy capacity is set to grow tenfold by 2050 in order to mitigate climate change. Unfortunately, wind turbine (WT) operation and maintenance (O&M) cost is twice as high as photovoltaic and fossil fuel power plants due to high frequency of very expensive unplanned repairs. The major areas of issue are rotating components of turbine rotor and drivetrain including blades, hub, low-speed shaft, main rotor bearing, gearbox and generator. Repair costs are increased by high crane hire cost, or even more expensive jack-up vessel charter costs (~£90k/day) for offshore WTs. WTs are equipped with condition monitoring (CM) systems to detect drivetrain faults, however no system in the market or in development monitors blades (a glass / carbon polymer structure), and hub, low-speed shaft without embedded sensors or non-destructive testing / inspection techniques. Embedded sensors are difficult to retrofit and maintain. Inspection requires shutdown in order to carry out non-destructive testing or manual inspections. Optical and IR cameras, robotic and drone solutions exist but they are expensive, cumbersome or limited in types of faults that can be detected. Manual inspections present health and safety problems due to tower climbs and rope access work. The project will develop a radically novel automatic online CM / root cause analysis system capable of detecting blade / hub / shaft dangerous faults without embedded sensors. Technology is underpinned by sophisticated signal processing and AI to deliver a cost effective, easily retrofitted solution. Blade / shaft / hub fault detection unlocks access to a \>$4.23 billion market opportunity. Based on projected sales, the industry will save £893 million over five years by enabling inexpensive planned repairs rather than costly replacements and preventing fault re-occurrence by root cause detection / elimination. System sales will generate cumulative PBIT £10.5M (5 years), ROI 14.3 and PBIT 378M (10 years).

This project will focus on the development of an intelligent battery operated sensor system for railway track and points displacement (movement) and temperature monitoring, offering multipoint to point wireless connectivity to a data acquisition unit located trackside and integrated with the current Network Rail maintenance monitoring network. Accurate signal analysis of dynamic displacement from accelerometers requires an innovative approach to meet with the signal processing restrictions of a low power environment, this combined with a mesh wireless topology to provide multipoint monitoring of railway points infrastructure brings together a number of established technologies in a cost effective manner to provide rail operators a condition monitoring tool in line with the 'Intelligent Infrastructure' strategy.

Awaiting Public Summary