A Damage Detection and Location Scheme for Offshore Wind Turbine Jacket Structures Based on Global Modal Properties

Structural failures of offshore wind substructures might be less likely than failures of other equipments of the offshore wind turbines, but they pose a high risk due to the possibility of catastrophic consequences. Significant costs are linked to offshore operations, like inspections and maintenance activities, thus remote monitoring shows promise for a cost-efficient structural integrity management. This work aims to investigate the feasibility of a two-level detection, in terms of anomaly identification and location, in the jacket support structure of an offshore wind turbine. A monitoring scheme is suggested by basing the detection on a database of simulated modal properties of the structure for different failure scenarios. The detection model identifies the correct anomaly based on three types of modal indicators, namely, natural frequency, the modal assurance criterion between mode shapes, and the modal flexibility variation. The supervised Fisher's linear discriminant analysis is applied to transform the modal indicators to maximize the separability of several scenarios. A fuzzy clustering algorithm is then trained to predict the membership of new data to each of the scenarios in the database. In a case study, extreme scour phenomena and jacket members' integrity loss are simulated, together with variations of the structural dynamics for environmental and operating conditions. Cross-validation is used to select the best hyperparameters, and the effectiveness of the clustering is validated with slight variations of the environmental conditions. The results prove that it is feasible to detect and locate the simulated scenarios via the global monitoring o f an offshore wind jacket structure.

Automated summary

This study aims to investigate the feasibility of detecting and locating anomalies in the jacket support structure of an offshore wind turbine. A monitoring scheme is proposed based on a database of simulated modal properties, and a supervised Fisher's linear discriminant analysis is used to transform modal indicators for anomaly identification. A fuzzy clustering algorithm is trained to predict the membership of new data. A case study demonstrates the effectiveness of the global monitoring in detecting and locating simulated scenarios.