A flutter boundary prediction method based on HHT, and machine learning is proposed to predict the flutter velocity before the wind speed reaches the subcritical state. Natural excitation technique is used to extract impulse response signals. EMD (empirical Mode decomposition method) is used to decompose the signal. Hilbert spectrum was obtained and analyzed by HHT to decompose the signal. The analysis methods included HHT spectrum and marginal spectrum analysis, so as to extract the characteristic quantity and establish the classification model according to different flight states. Then, regression models were established under different flutter modes for flutter degree analysis. During the prediction, according to the classification performance of the data to be measured, the flutter degree analysis result is weighted to obtain the flutter degree corresponding to the current wind speed, and then the flutter wind speed is calculated. In the selection of machine learning algorithm, naive Bayes algorithm, K-nearest neighbor algorithm and other machine learning algorithms are used to construct the classification model, linear regression, Gaussian process regression and so on are used to construct the regression model. The results show that the K-nearest neighbor algorithm performs best in the classification algorithm, while the Gaussian process regression algorithm performs best in the regression algorithm. Through the cross-validation of the test data, the proposed method can accurately predict the critical flutter velocity when it is far away from the flutter boundary through flutter mode recognition and flutter degree analysis.
KEYWORDS: Signal processing, Wind measurement, Signal attenuation, Detection theory, Turbulence, Signal to noise ratio, Error analysis, Electronic filtering, Data processing, Data modeling
A new method of mode parameter identification based on Extreme-point Symmetric Mode Decomposition (ESMD) and Matrix Pencil Method (MPM) is proposed for processing wind tunnel test data.The proposed method first decomposes the test data to a series of narrow-band signals by band-pass filtering.Then, the ESMD method is used to perform modal decomposition to obtain several single-mode response signals.Next, each singlemode response signal is processed using Natural Excitation Technique(NExT) to obtain a free attenuation response signals.Finally, the mode parameters were identified by the MPM.After the verification of simulation data, the proposed method is applied to identifying the mode parameters of the wind tunnel test data, and the results are compared with the mode parameter identification results based on the empirical mode decomposition (Empirical Mode Decomposition, EMD). The results show that the proposed method can better identify the mode parameters of the structure from the wind tunnel test data with good applicability and sufficient identification accuracy.
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