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.

冲击载荷是机械在工程应用中经常遇到的载荷。其时程重建和定位对于结构健康监测和可靠性分析至关重要。然而，在识别随机冲击载荷时，由于公式推导复杂、非线性结构不可行以及不适定问题，常规反演方法通常表现不佳。深度学习方法具有很强的特征学习和非线性表示能力以及全面的正则化机制。因此，提出了一种新的基于特征学习的方法来进行冲击载荷重建和定位。所提出的方法主要包括两部分。第一部分旨在重建冲击载荷，称为卷积-循环编码器-解码器神经网络 (ED-CRNN)。另一部分用于定位冲击载荷，称为深度卷积循环神经网络 (DCRNN)。ED-CRNN 利用一维 (1-D) 卷积编码器-解码器来获得输入信号的低维特征表示。两个长短期记忆 (LSTM) 层和一个双向 LSTM (BiLSTM) 层均匀分布在该网络中，以按时间步长学习输入特征与输出负载之间的关系。DCRNN 主要由两个一维卷积神经网络 (CNN) 层和两个 BiLSTM 层构建，以学习高隐藏级别的空间和时间特征。全连接层放置在末端以定位冲击载荷。两个数值研究和两个实验证明了所提出方法的有效性。结果表明，该方法能够准确、快速地重构和定位复杂装配结构的冲击载荷。此外，DCRNN 的性能与传感器数量和网络架构有关。同时，提出了交替布局的策略，以减少训练地点的数量。