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DEALING WITH HARMONICS AND NOISE FOR ADVANCED DYNAMIC IDENTIFICATION USING IN-FLIGHT HELICOPTER DATA

机译:使用飞行直升机数据处理高级动态识别的谐波和噪声

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In the framework of the HC-AG19 Garteur Action Group, novel methodologies and procedures are proposed and analyzed to improve finite element models of helicopters through in-flight dynamic data. Thus, the development of methods allowing accurate modal parameter estimation is essential to provide reliable reference data to the model updating process. Recently, a complete GVT test was performed by NLR, University of Bristol and Agusta Westland on a commercial helicopter, including shaker excitation with both random and pseudo-random signals, as well as acceleration profiles recorded during specific flight maneuvers. These provide an ideal data-set for method validation and development, in particular to verify the efficiency of Operational Modal Analysis versus standard Experimental Modal Analysis techniques. An additional challenge of the data set is that the recorded in-flight data contain both the harmonic contribution coming from the rotor as well as the noise which inevitably contaminate the data when measuring in operating conditions. In this paper, harmonic removal techniques and advanced identification methods will be applied on the data. The modal models will be firstly identified using Experimental Modal Analysis to obtain a reference dataset for validation. Operational Modal Analysis is then performed on the same data but neglecting the input force to validate the methods. Different harmonic removal techniques are also applied and their efficient in removing these components from the signals compared. Finally, the simulated in-flight GVT tests were repeated using as input recorded accelerations from different maneuvers (constant speed flight, hovering, landing and rotor stop). The results in some of these conditions will also be discussed.
机译:在HC-AG19 Garteur作用组的框架中,提出并分析了新的方法和程序,通过飞行动态数据来改善直升机的有限元模型。因此,允许准确的模态参数估计的方法的发展对于为模型更新过程提供可靠的参考数据是必不可少的。最近,由Bristol大学和Agusta Westland在商业直升机上进行了完整的GVT测试,包括随机和伪随机信号,以及在特定飞行中记录的加速曲线。这些提供了用于方法验证和开发的理想数据集,特别是验证操作模态分析与标准实验模态分析技术的效率。数据集的另一个挑战是记录的飞行中数据包含来自转子的谐波贡献以及在测量操作条件时不可避免地污染数据的噪声。本文将应用谐波去除技术和高级识别方法。将使用实验模态分析首先识别模态模型以获得用于验证的参考数据集。然后在相同的数据上执行操作模态分析,但忽略输入力以验证方法。还应用了不同的谐波去除技术,并且它们在比较信号中除去这些组件的有效方法。最后,使用来自不同机动的输入记录的加速度重复模拟的飞行中的GVT测试(恒速飞行,悬停,降落和转子停止)。还将讨论一些这些条件的结果。

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