Vibration data from physical systems, such as civil structures and machinery, often carries important information about the dynamic characteristics, but streaming acquisition of higher-frequency vibration often accrue large volumes of data, resulting in data transmission and storage challenges. Compressive sensing (CS) is a relatively newly-developed technique for efficient data representation, capable of reconstructing the target signal using only a few random measurements through sparse optimization. However, the real-world application of CS is hindered by the strong assumption of signal sparsity and a costly reconstruction process. In this work, we propose a novel deep learning method for vibration data reconstruction by using deep convolutional generative adversarial networks (DCGAN), which is composed of a generator $G$ and a discriminator $D$. A modified 1D symmetric U-net architecture with shortcuts is presented for $G$ to flexibly deal with different inputs, while a typical 1D classifier is used as $D$. A composite adversarial loss function is proposed considering errors in both time and frequency domains. The proposed DCGAN approach has several appealing properties. First, it directly learns the end-to-end mapping between the compressed and original signals without employing the sparsity assumption or random sampling, which fundamentally differs from existing sparsity-based CS methods. Second, the reconstruction process is highly computationally efficient as the network is fully feed-forward and no optimization is needed during data reconstruction. The proposed DCGAN approach is evaluated using the simulation data from a numerical 9-floor frame as well as experimental data collected from a large test steel grandstand. The results demonstrate the superiority of the proposed DCGAN in computational accuracy and efficiency compared to the tested sparsity-based algorithms. Furthermore, the influences of network configurations (network depth, down-sampling strategy, and shortcuts) are comprehensively explored.

Vibration data from physical systems, such as civil structures and machinery, often carries important information about the dynamic characteristics, but streaming acquisition of higher-frequency vibration often accrue large volumes of data, resulting in data transmission and storage challenges. Compressive sensing (CS) is a relatively newly-developed technique for efficient data representation, capable of reconstructing the target signal using only a few random measurements through sparse optimization. However, the real-world application of CS is hindered by the strong assumption of signal sparsity and a costly reconstruction process. In this work, we propose a novel deep learning method for vibration data reconstruction by using deep convolutional generative adversarial networks (DCGAN), which is composed of a generator $G$ and a discriminator $D$. 提出了一种改进的具有快捷方式的一维对称 U-net 架构$G$灵活处理不同的输入，而典型的一维分类器被用作$丁$. 考虑到时域和频域中的误差，提出了一种复合对抗损失函数。拟议的 DCGAN 方法具有几个吸引人的特性。首先，它直接学习压缩信号和原始信号之间的端到端映射，而无需采用稀疏性假设或随机采样，这与现有的基于稀疏性的 CS 方法根本不同。其次，重建过程具有很高的计算效率，因为网络是完全前馈的，并且在数据重建过程中不需要优化。使用来自数字 9 层框架的模拟数据以及从大型试验钢看台收集的实验数据评估所提出的 DCGAN 方法。结果表明，与经过测试的基于稀疏度的算法相比，所提出的 DCGAN 在计算精度和效率方面具有优越性。此外，还全面探讨了网络配置（网络深度、下采样策略和快捷方式）的影响。