WavLoadNet: Dynamic Load Identification for Aeronautical Structures Based on Convolution Neural Network and Wavelet Transform

Abstract

The accurate identification of dynamic load is important for the optimal design and fault diagnosis of aeronautical structures. Aiming at the identification of dynamic loads on complex or unknown aeronautical structures, a deep convolution neural network (CNN) in the transform domain-based method is proposed. It takes decomposed signals from wavelet transform of several vibration signals as input. A CNN is used for feature extraction, and fully connected layers are used for predicting the decomposed loads in the transform domain. After synthesizing the predicted decomposed components, the loads in the time domain can be obtained. The proposed method could avoid the explicit modeling of the system or transfer functions with complex or unknown structures. Using the data collected on a GARTEUR model, the proposed model is trained and verified. Extensive experimental results with qualitative and quantitative evaluations show the accuracy of this method and the robustness to measurement noise and other unknown load disturbances.

Keywords

• dynamic load identification
• wavelet transform
• transform domain
• one-dimensional convolution neural network