Integrated damage detection and time-series data augmentation for floating offshore mooring systems via variational semi-supervised learning

The dynamics and stability of the semi-submersible offshore platforms are significantly impacted by the degradation of the mooring system. Identifying structural integrity issues in mooring systems through a data-driven approach is challenging due to the infrequency of damage events and the difficulties in recording them. To address these challenges, this study proposes the Time-Series Variational Semi-Supervised Learning (TSVSSL) framework, which effectively bridges the gap between supervised and unsupervised learning by leveraging unlabelled data for damage detection. The proposed framework features a distinctive training procedure in which the encoder-decoder and classifier components are trained concurrently. This process produces a well-clustered latent representation that enhances damage detection and supports class-specific artificial data generation. A numerical study using simulated responses of a 5 MW semi-submersible FOWT under varying metocean conditions demonstrated that the proposed framework outperformed existing deep learning methods in damage detection, achieving superior accuracy, precision, recall, and F1 score. Further, a rejection sampling technique is also introduced to effectively generate artificial data that closely aligns with actual time series displacement response. The novelty of the proposed framework lies in its dual focus on damage detection and artificial data generation marking a significant advancement in the data-driven assessment of mooring systems.