A feature learning-based method for impact load reconstruction and localization of the plate-rib assembled structure

Abstract

Impact load is the load that machines frequently experienced in engineering applications. Its time-history reconstruction and localization are crucial for structural health monitoring and reliability analysis. However, when identifying random impact loads, conventional inversion methods usually do not perform well because of complex formula derivation, infeasibility of nonlinear structure, and ill-posed problem. Deep learning methods have great ability of feature learning and nonlinear representation as well as comprehensive regularization mechanism. Therefore, a new feature learning-based method is proposed to conduct impact load reconstruction and localization. The proposed method mainly includes two parts. The first part is designed to reconstruct impact load, named convolutional-recurrent encoder–decoder neural network (ED-CRNN). The other part is constructed to localize impact load, called deep convolutional-recurrent neural network (DCRNN). The ED-CRNN utilizes the one-dimensional (1-D) convolutional encoder–decoder to obtain low-dimension feature representations of input signals. Two long short-term memory (LSTM) layers and a bidirectional LSTM (BiLSTM) layer are uniformly distributed in this network to learn the relationship between input features and the output load in time steps. The DCRNN is constructed mainly by two 1-D convolutional neural network (CNN) layers and two BiLSTM layers to learn high-hidden-level spatial as well as temporal features. The fully connected layers are placed at the end to localize an impact load. The effectiveness of the proposed method was demonstrated by two numerical studies and two experiments. The results reveal that the proposed method has the ability to accurately and quickly reconstruct and localize the impact load of complex assembled structure. Furthermore, the performance of the DCRNN is related to the number of sensors and the architecture of the network. Meanwhile, the strategy of alternating layout is proposed to reduce the number of training locations.

Impact force identification on composite panels using fully overlapping group sparsity based on Lp-norm regularization

“…where u is a coefficient that changes with iteration. Since there are multiple local optimal solutions to Equation (17), the Nesterov's acceleration process is improved by adding a judgment step to achieve a continuous minimization of the objective function. From the above derivation, an AGSTA can be summarized as Algorithm 2.…”

“…Qiu et al 16 localized impact forces on a steel panel by using the pattern recognition method and reconstructed the time history of impact force via the Tikhonov regularization method, which are two separate processes. Chen et al 17 used two different deep neural networks for localization and time history reconstruction of the impact force separately, while a large amount of data was required to train the networks.…”

Deep learning-based methods in structural reliability analysis: a review

“…They have shown that their prediction accuracy was improved while maintaining the computational effort. Chen et al [77] proposed a feature-based DL method for impact load localization of a plate structure. They used two LSTM layers and a BiLSTM layer with uniform distribution to learn the connection between input and load in time steps.…”

“…After their work, other researchers tried to enhance their results. For example, Chen et al [77] proposed a feature learning-based method for impact load localization plate structures. They also used a Bi-LSTM and two LSTM layers to learn the relationship between inputs and output.…”

“…Using a hybrid CNN-LSTM, Chen et al [77] proposed a hybrid method in which its first part is designed to represent impact load via a 1D CNN, and the other part is designed to localize impact load, using LSTM. Their results show that their proposed method can accurately determine and localize the impact load of complex structures.…”