The current study focuses on early prediction of structural failure of a composite wind turbine blade (WTB) using acoustic emission (AE) and strain measurement. The structural response of a 14.3-m blade with embedded artificial defects is investigated under fatigue loading in flapwise direction. The fatigue loading is realized in several successive portions until structural failure. Strain and acoustic emission signals from each portion are recorded. The goal is to explore damage-sensitive features (DSFs) derived from acoustic emission and strain signals that would be suitable for early indication of blade failure under fatigue. These features include modal characteristics of strain time history, such as natural frequencies, damping ratios, and modal amplitudes. Acoustic emission features explored in this study comprise average frequency centroids based on an amplitude and absolute energy and gradients of cumulative energy curves. Changes of these features before failure relative to the previous loading portion are calculated and compared among different sensor locations with a twofold goal—firstly, to find the features that are the most sensitive to damage accumulation and, secondly, to find a location with the largest relative changes, thus enabling damage localization. The results show that strain and AE signals are correlated well in terms of pinpointing to a location of the largest aggregation of defects. This study gives recommendations of the most efficient feature combination of different measurements for reliable structural health monitoring of wind turbine blades.

中文翻译：

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用于风力涡轮机叶片早期故障预测的损伤敏感特征的控制

目前的研究重点是使用声发射 (AE) 和应变测量对复合风力涡轮机叶片 (WTB) 的结构故障进行早期预测。在襟翼方向的疲劳载荷下，研究了带有嵌入人工缺陷的 14.3 米叶片的结构响应。疲劳载荷在几个连续的部分中实现，直到结构失效。记录来自每个部分的应变和声发射信号。目标是探索源自声发射和应变信号的损伤敏感特征 (DSF)，这些特征适用于叶片疲劳失效的早期指示。这些特征包括应变时程的模态特性，例如固有频率、阻尼比和模态振幅。本研究中探索的声发射特征包括基于振幅和绝对能量以及累积能量曲线梯度的平均频率质心。计算失效前这些特征相对于先前加载部分的变化，并在不同传感器位置之间进行比较，具有双重目标——首先，找到对损伤累积最敏感的特征，其次，找到最大的位置相对变化，从而实现损伤定位。结果表明，应变和 AE 信号在精确定位最大缺陷聚集的位置方面具有良好的相关性。本研究提出了不同测量值的最有效特征组合的建议，以实现对风力涡轮机叶片的可靠结构健康监测。