Structural health monitoring for offshore wind turbines is imperative. Offshore wind energy is progressively attained at greater water depths, beyond 30 m, where jacket foundations are presently the best solution to cope with the harsh environment (extreme sites with poor soil conditions). Structural integrity is of key importance in these underwater structures. In this work, a methodology for the diagnosis of structural damage in jacket-type foundations is stated. The method is based on the criterion that any damage or structural change produces variations in the vibrational response of the structure. Most studies in this area are, primarily, focused on the case of measurable input excitation and vibration response signals. Nevertheless, in this paper it is assumed that the only available excitation, the wind, is not measurable. Therefore, using vibration-response-only accelerometer information, a data-driven approach is developed following the next steps: (i) the wind is simulated as a Gaussian white noise and the accelerometer data are collected; (ii) the data are pre-processed using group-reshape and column-scaling; (iii) principal component analysis is used for both linear dimensionality reduction and feature extraction; finally, (iv) two different machine-learning algorithms, k nearest neighbor (k-NN) and quadratic-kernel support vector machine (SVM), are tested as classifiers. The overall accuracy is estimated by 5-fold cross-validation. The proposed approach is experimentally validated in a laboratory small-scale structure. The results manifest the reliability of the stated fault diagnosis method being the best performance given by the SVM classifier.

中文翻译：

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夹克式海上风力发电机的结构健康监测：概念的实验证明。

海上风力发电机的结构健康监测势在必行。在更大的水深（超过30 m）中逐渐获得海上风能，目前，外套基础是应对恶劣环境（土壤条件恶劣的极端地区）的最佳解决方案。在这些水下结构中，结构完整性至关重要。在这项工作中，提出了一种用于诊断外套式基础中结构损坏的方法。该方法基于以下准则：任何损坏或结构变化都会导致结构的振动响应发生变化。该领域的大多数研究主要集中于可测量的输入激励和振动响应信号的情况。然而，在本文中，假设唯一可测量的激励是风，是无法测量的。因此，使用仅振动响应的加速度计信息，按照以下步骤开发了一种数据驱动的方法：（i）将风模拟为高斯白噪声，并收集加速度计数据；（ii）使用组重整和列缩放对数据进行预处理；（iii）主成分分析用于线性降维和特征提取；最后，（iv）测试了两种不同的机器学习算法，即k最近邻居（k-NN）和二次核支持向量机（SVM）作为分类器。总体准确性通过5倍交叉验证进行估算。所提出的方法在实验室的小型结构中进行了实验验证。结果表明，所述故障诊断方法的可靠性是SVM分类器提供的最佳性能。