Dynamic load identification plays an important role in the field of fault diagnosis and structural modification design for aircraft. In conventional dynamic load identification approaches, accurate structural modeling is usually needed, which is difficult to obtain for highly nonlinear or unknown structures. In this paper, a one-dimensional convolution neural network with multiple modules is proposed for random dynamic load identification of aircraft. Firstly, the convolution module is designed for temporal feature extraction. Secondly, the extracted features are linearly weighted based on the contributions to the final output. The contributions are learned in a data driven manner via the designed attention module. Lastly, the dynamic load of a certain time stamp is predicted from the learned and weighted features. The proposed model is trained and tested using the real data from a GARTEUR aircraft model. Extensive experimental results with qualitative and quantitative evaluations have demonstrated the identification performance with satisfactory accuracy of the proposed approach under different strengths of load noises.

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.