Abstract
Within this paper, an approach to monitoring the rotational performance of weathervaning systems is proposed, by mainly looking at the so-called release events: the maximum rotation (or break-out angle) of the geostationary side of the turret, where the mooring yaw stiffness overcomes the static friction moment internal to the rotational interfaces of the floating facility. The physics of the stick-slip and release behavior are reiterated, the multiple dependencies are identified, and various performance characteristics are defined which will support long term operational monitoring of the turret’s weathervaning capacity.
Practical methods and examples are presented to individually monitor the governing dependencies. In the end, an unsupervised machine learning algorithm is proposed to identify any unwarranted increases in break-out angle: an anomaly detection method based on isolating the contribution of each of the dependencies on the release event dynamics.