Statistical times series based damage detection in the fiber rope mooring lines of the semi-submersible OO-STAR wind floater

The Floating Offshore Wind Turbines (FOWTs) based on semi-submersible floaters constitute a popular choice in most markets due to their installation being flexible and in need of low infrastructural requirements. A simple and robust three-legged semi-submersible floater for FOWTs, the OOSTAR wind floater, has been introduced and it can be anchored to the seabed with steel chain mooring lines or hybrid mooring lines - a combination of chains and synthetic fiber ropes. The fiber rope mooring lines present a number of advantages thus leading to a lighter and less costly mooring system. These lines are important for the FOWT’s integrity as their loss can lead to the change of the floater’s position, a damaged power cable, a possible collision with other infrastructure and high maintenance costs. This why an early detection of damages in the mooring system is crucial. In this study, damage detection in the main part of fiber rope mooring lines of semi-submersible based FOWTs is investigated for the first time. In particular, the OO-STAR floater based FOWT is considered. Two Statistical Time Series based detection methods, the Multiple Model- AutoRegressive (MM-AR) method and the Functional Model Based Method (FMBM) are used and compared. The MM-AR is based on multiple AR models whereas the FMBM on a single Functional Model, for the description of the healthy FOWT’s dynamics under varying environmental conditions. The results based on seven healthy and eight damage cases under varying wind speed and wave height show that the two methods are able to achieve damage detection in fiber rope mooring lines without any false alarm or missed damage despite of damages having small effects on the FOWT’s dynamics and the fiber ropes presenting a non-linear behaviour.