Structural health monitoring of offshore wind turbines: A review
through the Statistical Pattern Recognition Paradigm
Offshore Wind has become the most profitable renewable energy source due to the remarkable development
it has experienced in Europe over the last decade. In this paper, a review of Structural Health
Monitoring Systems (SHMS) for offshore wind turbines (OWT) has been carried out considering the topic
as a Statistical Pattern Recognition problem. Therefore, each one of the stages of this paradigm has been
reviewed focusing on OWT application. These stages are: Operational Evaluation; Data Acquisition,
Normalization and Cleansing; Feature Extraction and Information Condensation; and Statistical Model
Development. It is expected that optimizing each stage, SHMS can contribute to the development of
efficient Condition-Based Maintenance Strategies. Optimizing this strategy will help reduce labor costs of
OWTs' inspection, avoid unnecessary maintenance, identify design weaknesses before failure, improve
the availability of power production while preventing wind turbines' overloading, therefore, maximizing
the investments' return. In the forthcoming years, a growing interest in SHM technologies for OWT is
expected, enhancing the potential of offshore wind farm deployments further offshore. Increasing effi-
ciency in operational management will contribute towards achieving UK's 2020 and 2050 targets,
through ultimately reducing the Levelised Cost of Energy (LCOE).