Subsea power cables are critical assets for electrical transmission and distribution networks, and highly relevant to regional, national, and international energy security and decarbonization given the growth in offshore renewable energy generation. Existing condition monitoring techniques are restricted to highly constrained online monitoring systems that only prioritize internal failure modes, representing only 30% of cable failure mechanisms, and has limited capacity to provide precursor indicators of such failures or damages. To overcome these limitations, we propose an innovative fusion prognostics approach that can provide the in situ integrity analysis of the subsea cable. In this paper, we developed low-frequency wide-band sonar (LFWBS) technology to collect acoustic response data from different subsea power cable sample types, with different inner structure configurations, and collate signatures from induced physical failure modes as to obtain integrity data at various cable degradation levels. We demonstrate how a machine learning approach, e.g., SVM, KNN, BP, and CNN algorithms, can be used for integrity analysis under a hybrid, holistic condition monitoring framework. The results of data analysis demonstrate the ability to distinguish subsea cables by differences of 5 mm in diameter and cable types, as well as achieving an overall 95%+ accuracy rate to detect different cable degradation stages. We also present a tailored, hybrid prognostic and health management solution for subsea cables, for cable remaining useful life (RUL) prediction. Our findings addresses a clear capability and knowledge gap in evaluating and forecasting subsea cable RUL. Thus, supporting a more advanced asset management and planning capability for critical subsea power cables.

Wind turbine blades (WTBs) are critical sub-systems consisting of composite multi-layer material structures. WTB inspection is a complex and labour intensive process, and failure of it can lead to substantial energy and economic losses to asset owners. In this paper, we proposed a novel non-destructive evaluation method for blade composite materials, which employs Frequency Modulated Continuous Wave (FMCW) radar, robotics and machine learning (ML) analytics. We show that using FMCW raster scan data, our ML algorithms (SVM, BP, Decision Tree and Naïve Bayes) can distinguish different types of composite materials with accuracy of over 97.5%. The best performance is achieved by SVM algorithms, with 94.3% accuracy. Furthermore, the proposed method can also achieve solid results for detecting surface defect: interlaminar porosity with 80% accuracy overall. In particular, the SVM classifier shows highest accuracy of 92.5% to 98.9%. We also show the ability to detect air voids of 1mm differences within the composite material WT structure with 94.1% accuracy performance using SVM, and 84.5% using Naïve Bayes. Lastly, we create a digital twin of the physical composite sample to support the integration and qualitative analysis of the FMCW data with respect to composite sample characteristics. The proposed method explores a new sensing modality for non-contact surface and subsurface for composite materials, and offer insights for developing alternative, more cost-effective inspection and maintenance regimes.

Over recent years, the emergence of the offshore wind sector has spurred much interest in subsea cables. The predominant failure modes of subsea cables are associated with extreme environmental conditions. Wave-forcing during severe storms is less expected and causes more damage. A generalized multiphysics cable model is constructed to predict the movement, damage, and lifetime of subsea cables subject to dynamic wave and current action due to abrasion and corrosion. The present cable lifespan prediction model extended the previous tide-only model by considering the contribution of hydrodynamic forces by waves and the effect of wave and current incident angle relative to the cable. The predicted cable sliding distance at each section of the cable is combined with the Archard abrasion wear model and the corrosion model to predict the loss of cable protective layers and the resulting expected lifespan of the cable. The model is the first of its kind that can predict the spatial variation of wave and current loading, cable movement, damage, remaining lifetime, and cable failure modes and location. In addition, spatial and temporal variations of magnitude and direction of wave, current, and tide can be incorporated into the model for realistic large-scale simulations of cable performance in field conditions. The model compares well with previous laboratory experiments and numerical models. The present model was applied for the first time to the European Marine Energy Centre (EMEC)’s wave test site located at Billia Croo off the west coast of mainland Orkney, Scotland, and validated by the cable lifespan data. The 1-year and 100-year return period wave height and period and the average wave and tide conditions are used to drive the present cable lifespan model. It was found that the cable movement is predominantly driven by waves, and the previous tide-only model would predict zero cable movement, indicating the importance of the incorporation of wave contribution into the cable model. Furthermore, besides wave height and period, the wave angle relative to cable was found to be a determining factor for the cable movement and lifespan. The present multiphysics cable model provides a new capability to predict 70% of failure modes currently not monitored in situ and to deploy, plan, and manage subsea cables with improved fidelity, reduced cost, and human risk.