Condition-based structural health monitoring of offshore wind jacket structures: Opportunities, challenges, and perspectives

Abstract

Structural health monitoring (SHM) has been recognized as a useful tool for safety management and risk reduction of offshore wind farms. In complex offshore environment, jacket structures of offshore wind turbines are prone to damages due to corrosion and fatigue. Effective SHM on jacket structures can substantially reduce their operation risk and costs. This work reviews the latest progress on the SHM of offshore wind jacket structures. The achievements in the structural damage identification, location, quantification, and remaining useful life (RUL) estimation are respectively introduced in detail, and existing challenges are discussed. Possible solutions to the challenges using the Digital Twin (DT) technology are put forward. The DT is able to mirror a real jacket structure into a virtual model, and Bayesian updating can refresh the virtual model parameters in real-time to keep consistency between virtual model and physical structure; then, just-in-time SHM can be carried out for jacket structures by performing damage detection, location, quantification, and RUL estimation using the virtual model. As a result, the DT may provide engineers and researchers a practicable tool for safety monitoring and risk reduction of fixed foundation offshore wind structures.

Influence of Smart Sensors on Structural Health Monitoring Systems and Future Asset Management Practices

“…Liu and Ren have demonstrated a rapid acquisition method for structural stress in SHM of a ship hull where they have achieved a maximum error of 0.0005% in structural yield strength [95]. Leng et al have proposed a condition-based SHM system for off-shore wind jacket structures [96]. The submarine infrastructure of offshore wind turbines always faces harsh working environments due to unpredictable ocean conditions and this DT system will provide the opportunity to estimate the remaining lifetime of the asset more accurately.…”

Enhancing response estimation and system identification in structural health monitoring through data-driven approaches

“…29 The opportunities, challenges, and perspectives of new condition-based structural health monitoring approaches of offshore structures was studied to present the application of these new advancements in the field of SHM. 30 Additionally, intelligent fault diagnosis has been continuously concerned using different advanced techniques such adaptive variational mode extraction method, 31 data-driven dictionary learning method, 32 Synchroextracting frequency chirplet transform, 33 and data reconstruction methods. 6,34 This study introduces a simplified data-driven approach, incorporating ML-based techniques, to identify unknown vibrations in a multi-story building.…”

Enhancing Lamb wave-based damage diagnosis in composite materials using a pseudo-damage boosted convolutional neural network approach

“…Companies in several fields are increasingly integrating Structural Health Monitoring (SHM) systems into maintenance programs, to move toward more efficient condition-based strategies. [1][2][3] In this context, diagnosis methods based on the analysis of Ultrasonic Guided Waves (UGWs) have been consistently used to monitor the health state of thin-walled structures. Although traditional methods have been successfully applied to detect and localize damage, their performance is usually conditioned by the manual features extraction process required for the definition of the damagesensitive features, or damage indices (DIs).…”

“…Recently, in this field, the outbreak of machine learning techniques has brought interesting advancements. [1][2][3]4 The employment of artificial neural networks has been shown to provide a valid alternative to traditional imaging methods. To date, most of the methods proposed in the literature have employed traditional feed-forward neural networks (FFNNs) or convolutional neural networks (CNNs).…”