Operations and Maintenance in Offshore Wind is based currently on old models generated in the maritime sector, where routine, scheduled maintenance is carried out periodically. Industry insight has indicated a strong desire to transition to a predictive maintenance framework, based on accurate condition monitoring of individual components within an offshore wind asset. A key reason for this need is the prohibitive cost that offshore maintenance commands. Currently 20-30% of the overall expenditure for an offshore wind farm is based on Operations and Maintenance, and as the number of offshore wind farms increases and becomes even more remote, the resources (human expertise, CTV's and SOV's) will become more limited, and costs could therefore increase further.

The current state of the art in the field offer sensors for monitoring aspects of mechanical systems such as vibration and acoustic emissions. A key challenge in this area is the communication required for transfer of data back to the control centre. FLICQ have developed Smart Sensor technology based on Industrial Internet of Things technology that not only collects data from the assets, but can also process and analyse sensor data to extract the meaningful information and minimise the amount of data required to be transferred back onshore to the cloud or to legacy systems. To carry out this data analysis, the FLICQ technology uses machine learning algorithms to pre-process this data for transmission. By accurately modelling how asset condition is changing, predictive models can be used to plan the best intervention time based on risk, anticipated downtime, and projected revenue loss.

This project aims to demonstrate the potential of FLICQ smart systems to support the development of predictive maintenance strategies in the offshore wind sector through a practical application and product development at Levenmouth offshore demonstration turbine, owned by ORE Catapult. While FLICQ sensor technologies have been employed in a range of other sectors, the technology is yet to be implemented in the challenging offshore wind environment. The innovative technology proposed by FLICQ uses intuitive machine learning algorithms where the data is collected. This means that the data connection required for transmission to onshore control centres is low-bandwidth and low data usage. This allows the sensor to remain low-powered and have a long life in position on any component. The success FLICQ have experienced in other markets represents an opportunity to disrupt maintenance strategies in offshore wind using disruptive, Smart Sensors, powered by enabling technology.