Due to the inevitable deviations between the results of theoretical calculations and physical experiments, flutter tests and flutter signal analysis often play significant roles in designing the aeroelasticity of a new aircraft. The measured structural response from aeroelastic models in both wind tunnel tests and real fight flutter tests contain an abundance of structural information, but traditional methods tend to have limited ability to extract features of concern. Inspired by deep learning concepts, a novel feature extraction method for flutter signal analysis was established in this study by combining the convolutional neural network (CNN) with empirical mode decomposition (EMD). It is widely hypothesized that when flutter occurs, the measured structural signals are harmonic or divergent in the time domain, and that the flutter modal (1) is singular and (2) its energy increases significantly in the frequency domain. A measured-signal feature extraction and flutter criterion framework was constructed accordingly. The measured signals from a wind tunnel test were manually labeled “flutter” and “no-flutter” as the foundational dataset for the deep learning algorithm. After the normalized preprocessing, the intrinsic mode functions (IMFs) of the flutter test signals are obtained by the EMD method. The IMFs are then reshaped to make them the suitable size to be input to the CNN. The CNN parameters are optimized though the training dataset, and the trained model is validated through the test dataset (i.e., cross-validation). The accuracy rate of the proposed method reached 100% on the test dataset. The training model appears to effectively distinguish whether or not the structural response signal contains flutter. The combination of EMD and CNN provides effective feature extraction of time series signals in flutter test data. This research explores the connection between structural response signals and flutter from the perspective of artificial intelligence. The method allows for real-time, online prediction with low computational complexity.

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基于EMD和CNN的颤振测试特征提取方法研究

由于理论计算结果和物理实验之间不可避免的偏差，颤振测试和颤振信号分析通常在设计新飞机的空气弹性方面起着重要作用。在风洞试验和实际扑扑试验中从气动弹性模型测得的结构响应都包含大量的结构信息，但是传统方法提取受关注特征的能力往往有限。受深度学习概念的启发，本研究通过将卷积神经网络（CNN）与经验模式分解（EMD）相结合，建立了一种用于颤振信号分析的新颖特征提取方法。广泛假设，当发生抖动时，测得的结构信号在时域中是谐波或发散的，并且颤振模态（1）是奇异的，并且（2）它的能量在频域中显着增加。相应地构建了测量信号特征提取和颤振准则框架。来自风洞测试的测量信号被手动标记为“颤振”和“无颤振”，作为深度学习算法的基础数据集。在标准化的预处理之后，通过EMD方法获得颤动测试信号的本征模式函数（IMF）。然后，将IMF整形，使其具有适合输入CNN的大小。通过训练数据集优化CNN参数，并通过测试数据集验证训练后的模型（即交叉验证）。所提方法在测试数据集上的准确率达到100％。训练模型似乎可以有效地区分结构响应信号是否包含颤动。EMD和CNN的组合提供了抖动测试数据中时间序列信号的有效特征提取。这项研究从人工智能的角度探讨了结构响应信号与颤动之间的联系。该方法允许以低计算复杂度进行实时在线预测。