Abstract
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.
Highlights
Flutter is a destructive aeroelastic instability produced by the coupling of aerodynamic, inertial, and elastic forces in the elastic structure of an aeroelastic system
The structure response signal and flutter criterion were investigated in this study from the perspective of signal analysis based on a deep learning algorithm and empirical mode decomposition (EMD) method in an effort to remedy these problems
In order to minimize any artificial error arising in the acquisition of actual wind tunnel test data, the flutter test signal xðnÞ is subjected to zero-average preprocessing to obtain the signal yðnÞ; yðnÞ is subjected to EMD to obtain its intrinsic mode functions (IMFs)
Summary
Flutter is a destructive aeroelastic instability produced by the coupling of aerodynamic, inertial, and elastic forces in the elastic structure of an aeroelastic system. This paper introduces a data-driven deep learning method for extracting flutter characteristics from the structural response signals of aeroelastic models. The time-frequency diagram is input to the CNN, which performs feature extraction and classification This combination has proven somewhat effective; the STFT has some notable drawbacks [18]:. The time series intrinsic mode function (IMF) obtained by EMD can be input to the CNN to perform feature extraction and realize signal classification. Flutter test signal features were extracted based on the CNN from a large dataset to develop a trained model. The CNN and EMD were combined to process time series signals for flutter test signal analysis. The CNN extracts features through a series of convolution and pooling operations, implements the classification using fully connected layers and loss function calculations
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
