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Publications
Yeter, B., Garbatov, Y. and Guedes Soares, C. (2021), “Structural Health Monitoring Data Analysis for Ageing Fixed Offshore Wind Turbine Structures”, 40th International Conference on Ocean, Offshore and Arctic Engineering (OMAE2021), 21-30 June, Virtual online, Paper OMAE2021-63007.
The present study aims to perform a systematical data analysis of structural health monitoring for ageing fixed offshore wind turbine support structures. The life-cycle extension of the first offshore wind farms in operation is under serious consideration since the support structures are still in a condition to be used further. Big data analytics and machine learning techniques can aid to extract useful information from the monitoring data collected during the service life and build models for future predictions of an optimal life-extension. To this end, it is aimed to analyze the big data provided by embedded control systems and non-destructive inspections of ageing offshore wind turbine support structures using pre-processing techniques, including denoising, detrending, and filtering to remove the noise of different nature and seasonality as well as to detect the signal-specific contents affecting the structural integrity in the time and frequency domain. The Welch method’s effectiveness is investigated in terms of dealing with noisy signals in the frequency domain. The principal component analysis is also carried out to reduce the dimensionality of the data and select the most significant features responsible for the spread in the structural health monitoring data. Moreover, nonparametric statistical methods are used to test whether the data before noise is added and the data after cleansing the added noise from the population with the same distribution. Further, permutation (randomization) testing is performed to predicate that the nonparametric test results are statistically significant. The outcome of this study provides refined evidence that enables to feed the condition monitoring data into the training of the deep neural network to be able to discriminate different structural conditions.
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