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Kalman Filter Based Data Fusion for Bi-Axial Neutral Axis Tracking in Wind Turbine Towers

机译:基于卡尔曼滤波器的数据融合在风力发电机塔架双轴中性轴跟踪中的应用

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Structural Health Monitoring (SHM) systems allow early detection of damage which allows maintenance planning and reduces the maintenance cost. So there is a lot of active research in the area for SHM of civil and mechanical structures. The SHM system uses a damage sensitive feature, and any change in the structure is reflected by a change in this feature. If this change is above a threshold the structure is said to be damaged. In most applications the determination of this threshold is based on engineering judgment and the previous experience of the operator. These practices are highly subjective to the skill of the operator and the uncertainty is more in case of unique structures or in unique conditions, like in the case of the newer high capacity wind turbines. So there is a need for a formal methodology for the selection of the thresholds for the chosen damage detection technique. This paper demonstrates a methodology for the selection of threshold for damage detection based on qualitative data acquired from several damage scenarios of a 10 MW wind turbine. The damage indicator is the change of neutral axis (NA) which is tracked using Kalman Filter (KF). Based on the level of damage to be detected and the probability of occurrence of false positive and false negative detections, a threshold value is selected. This threshold is then applied to strain data from the Nordtank NTK500/41 wind turbine for validation.
机译:结构健康监测(SHM)系统允许及早发现损坏,从而可以进行维护计划并降低维护成本。因此,在土木和机械结构的SHM领域有很多积极的研究。 SHM系统使用损坏敏感功能,该功能的更改反映了结构的任何变化。如果该变化高于阈值,则认为该结构已损坏。在大多数应用中,此阈值的确定是基于工程判断和操作员的先前经验。这些做法对操作人员的技能具有很高的主观性,并且在独特的结构或独特的条件下(如在更新的大容量风力涡轮机中),不确定性更大。因此,需要一种正式的方法来选择所选择的损伤检测技术的阈值。本文演示了一种基于从10兆瓦风力涡轮机的几种损坏情景中获取的定性数据来选择损坏检测阈值的方法。损坏指示器是使用卡尔曼滤波器(KF)跟踪的中性轴(NA)的变化。基于要检测的损坏程度以及错误肯定和错误否定检测的发生概率,选择阈值。然后将此阈值应用于来自Nordtank NTK500 / 41风力涡轮机的应变数据以进行验证。

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