Semi-Supervised Domain-Adversarial Mooring Damage Detection for Floating Offshore Wind Turbines Under Unseen Sea States

Abstract

The mooring system is the dominant single point of failure on a floating offshore wind turbine, and inspecting it drives much of its operations and maintenance cost. Data-driven damage classifiers could ease that burden, but they are usually trained and tested on one sea-state distribution, so accuracy drops for the severe conditions that lie outside it. This work presents a domain-adversarial gated recurrent unit (GRU) that identifies eleven mooring conditions from the platform’s six rigid-body motions and transfers, under a semi-supervised protocol, to sea states for which no damage labels are available in training. The conditions span a healthy baseline, single-line and multi-line stiffness loss, non-uniform degradation, and two biofouling severities; all are sub-failure damage cases whose motion signatures are weak and easily confused. The model is trained on operational sea states and tested on unseen extreme storm states (22–26 m/s wind, 8.0–9.5 m significant wave height), simulated in OpenFAST with MoorDyn on the IEA 15 MW UMaine VolturnUS-S platform. A three-phase schedule combining a labeled intermediate near-target domain with adversarial alignment of the training and test feature distributions raises per-simulation macro-F1 across three seeds to 0.87, against 0.29 for a source-only model, 0.47 for the near-target bridge alone and 0.73 for adversarial alignment alone. Neither component reaches this level by itself: alignment supplies the larger share of the gain and the bridge stabilizes it, reducing seed-to-seed variability roughly fourfold. The gain persists under sensor noise up to 20% of the per-channel standard deviation, and a parameter-matched 1D-CNN backbone confirms it is not tied to the recurrent architecture. The setting is semi-supervised, with respect to which target domain labels are available for the source and the intermediate near-target range, while the target sea states contribute unlabeled windows only. Because it requires no labels at the target sea states and only the standard six-degree-of-freedom motion sensors, the method can be extended to the severe, unseen simulated sea states that an asset meets in service; labeled near-target simulations from a calibrated platform model are still required. The present study is a simulation-based feasibility study: all evidence derives from OpenFAST and MoorDyn simulations of a single platform, and validation against tank-test or field measurements remains to be carried out.