Towards unsupervised fault detection for offshore wind turbine cable protection systems using contrastive learning

Abstract As offshore wind power expands globally, it is essential to ensure the reliable operation of components of such critical infrastructures. A less explored instance of such components, which are though essential in terms of operation, is found in subsea turbine cables and their protection systems, whose failure can incur prolonged shutdown periods and costly repairs. We propose a novel unsupervised machine learning approach exploiting use of Distributed Acoustic Sensing (DAS) data and contrastive learning for monitoring offshore wind turbine Cable Protection Systems (CPSs). A Transformer neural network adapted for time-series ingests the high-frequency, noisy DAS CPS time-series measurements, and is trained to learn a coherent representation of the data using a contrastive learning scheme that enforces temporal and positional consistency in the latent space. This latent representation can then be used to perform anomaly detection in an unsupervised manner, alleviating the need for costly labeled offshore anomaly data. We demonstrate that a coherent representation of the data is learnt by the model, which we then use to detect synthetic anomalies and an actual CPS stabilization event.