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Damage assessment for wind turbine blades based on a multivariate statistical approach

机译:基于多元统计方法的风机叶片损伤评估

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摘要

This paper presents a vibration based structural health monitoring methodology for damage assessment on wind turbine blades made of composite laminates. Normally, wind turbine blades are manufactured by two half shells made by composite laminates which are glued together. This connection must be carefully controlled due to its high probability to disbond which might result in collapse of the whole structure. The delamination between both parts must be monitored not only for detection but also for localisation and severity determination. This investigation consists in a real time monitoring methodology which is based on singular spectrum analysis (SSA) for damage and delamination detection. SSA is able to decompose the vibratory response in a certain number of components based on their covariance distribution. These components, known as Principal Components (PCs), contain information about of the oscillatory patterns of the vibratory response. The PCs are used to create a new space where the data can be projected for better visualization and interpretation. The method suggested is applied herein for a wind turbine blade where the free-vibration responses were recorded and processed by the methodology. Damage for different scenarios viz diferent sizes and locations was introduced on the blade. The results demonstrate a clear damage detection and localization for all damage scenarios and for the different sizes.
机译:本文提出了一种基于振动的结构健康监测方法,用于评估复合材料层压板制成的风力涡轮机叶片的损伤。通常,风力涡轮机叶片由两个半壳制成,该两个半壳由粘合在一起的复合层压板制成。由于该连接很可能会松开,因此可能会导致整个结构崩溃,因此必须仔细控制此连接。两个部分之间的分层不仅必须进行监控以进行检测,而且还必须进行定位和严重性确定。这项研究包括一种实时监测方法,该方法基于奇异频谱分析(SSA)进行损坏和分层检测。 SSA能够根据其协方差分布分解某些组件中的振动响应。这些称为主成分(PC)的成分包含有关振动响应的振荡模式的信息。 PC用于创建新的空间,可以在其中投影数据以更好地进行可视化和解释。本文所建议的方法适用于风力涡轮机叶片,其中通过该方法记录并处理了自由振动响应。刀片上引入了针对不同场景的损坏,即大小和位置不同。结果表明,对于所有损坏情况和不同大小,都可以进行清晰的损坏检测和定位。

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