Flutter flight test involves the evaluation of the airframe’s aeroelastic stability by applying artificial excitation on the aircraft lifting surfaces. The subsequent responses are captured and analyzed to extract the frequencies and damping characteristics of the system. However, noise contamination, turbulence, non-optimal excitation of modes, and sensor malfunction in one or more sensors make it time-consuming and corrupt the extraction process. In order to expedite the process of identifying and analyzing aeroelastic modes, this study implements a time-delay embedded Dynamic Mode Decomposition technique. This approach is complemented by Robust Principal Component Analysis methodology, and a sparsity promoting criterion which enables the automatic and optimal selection of sparse modes. The anonymized flutter flight test data, provided by the fifth author of this research paper, is utilized in this implementation. The methodology assumes no knowledge of the input excitation, only deals with the responses captured by accelerometer channels, and rapidly identifies the aeroelastic modes. By incorporating a compressed sensing algorithm, the methodology gains the ability to identify aeroelastic modes, even when the number of available sensors is limited. This augmentation greatly enhances the methodology’s robustness and effectiveness, making it an excellent choice for real-time implementation during flutter test campaigns.

中文翻译：

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一种基于有限传感器测量的颤振飞行测试中气动弹性模式快速检测的数据驱动方法


颤振飞行试验涉及通过对飞机升力面施加人工激励来评估机身的气动弹性稳定性。随后的响应被捕获并分析，以提取系统的频率和阻尼特性。然而，噪声污染、湍流、非最佳模式激励以及一个或多个传感器中的传感器故障使得提取过程变得耗时且破坏。为了加快识别和分析气动弹性模态的过程，本研究实施了时滞嵌入式动态模态分解技术。这种方法得到了鲁棒主成分分析方法和稀疏性促进标准的补充，该标准能够自动和最佳地选择稀疏模式。本次实现中使用了本文第五作者提供的匿名颤振飞行测试数据。该方法假设不知道输入激励，仅处理加速度计通道捕获的响应，并快速识别气动弹性模式。通过结合压缩传感算法，即使可用传感器的数量有限，该方法也能够识别气动弹性模式。这种增强极大地增强了该方法的稳健性和有效性，使其成为颤振测试活动期间实时实施的绝佳选择。