Random Dynamic Load Identification with Noise for Aircraft via Attention Based 1D-CNN

Abstract

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.

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

“…The load would cause the vibration on the structure and the vibration response are measured at N different measure points on the structure. Suppose that the N measured responses are the acceleration signals, which are relatively easy to measure and widely used in different load identification works [23,25]. Let a i (t) be the measured discrete acceleration response in the time domain of the i-th measurement point.…”

“…Besides the regularization-based method, the artificial intelligence-based algorithms, or the intelligent algorithm, would provide another approach to identify the loads, for instance, the support vector machines-based regression method [17], the ensemble learningbased method [18] or the neural network (NN)-based method [19][20][21][22][23][24][25][26][27]. These approaches implicitly learn the mapping relationship between loads and responses in a data-driven manner, which weakens the dependence on the accurate and time-consuming modeling of structural transfer function and parameter selection in conventional regularizationbased methods, and promotes the application of load identification methods in complex or unknown structural applications.…”

“…Xia et al [24] propose to use the time delay neural network (TDNN), which is equivalent to a one layer CNN for a sequential input, to identify the dynamic loads for an aircraft rudder model. He et al [25] apply a CNN with an attention mechanism to identify random loads on aircraft structures under noise disturbance conditions. The attention mechanism can better fuse features extracted by the network and improve the accuracy of load identification; however, the attention module would introduce additional parameters and computing costs.…”

“…Experimental results in [29] show that a better identification accuracy can be obtained in the transform domain than that in the frequency domain using a regularization-based method. In this paper, we tend to address the problem of dynamic load identification with an NN-based method in the transform domain, unlike the time-domain-based NN method [23,25,27]. The network would take the decomposed components of several responses in a time window in the transform domain, e.g., the wavelet domain, as the input.…”

Dynamic Load Identification at Natural Frequencies for Aircraft via Attention Based 1D-CNN

“…Compared with traditional artificial neural networks, Attention Based 1D-CNN can extract information from signals by means of convolution, support multi-channel signal extraction, avoid complex pre-processing of multi-channel signals, and can directly input the original signal, thus preserving the spatial information in the multi-channel signal. The detailed process of the calculation and the parameters of the deep learning model can be referred to our other study [20].…”