Wang Zhiwen, Sun Shouwang, Li Yiwei, Yue Zixiang, Ding Youliang
Key Laboratory of Concrete and Pre-Stressed Concrete Structures of the Ministry of Education, Southeast University, Nanjing 210096, China.
YunJi Intelligent Engineering Co., Ltd., Shenzhen 518000, China.
Sensors (Basel). 2023 Jun 17;23(12):5661. doi: 10.3390/s23125661.
Signal transmission plays an important role in the daily operation of structural health monitoring (SHM) systems. In wireless sensor networks, transmission loss often occurs and threatens reliable data delivery. The massive amount of data monitoring also leads to a high signal transmission and storage cost throughout the system's service life. Compressive Sensing (CS) provides a novel perspective on alleviating these problems. Based on the sparsity of vibration signals in the frequency domain, CS can reconstruct a nearly complete signal from just a few measurements. This can improve the robustness of data loss while facilitating data compression to reduce transmission demands. Extended from CS methods, distributed compressive sensing (DCS) can exploit the correlation across multiple measurement vectors (MMV) to jointly recover the multi-channel signals with similar sparse patterns, which can effectively enhance the reconstruction quality. In this paper, a comprehensive DCS framework for wireless signal transmission in SHM is constructed, incorporating the process of data compression and transmission loss together. Unlike the basic DCS formulation, the proposed framework not only activates the inter-correlation among channels but also provides flexibility and independence to single-channel transmission. To promote signal sparsity, a hierarchical Bayesian model using Laplace priors is built and further improved as the fast iterative DCS-Laplace algorithm for large-scale reconstruction tasks. Vibration signals (e.g., dynamic displacement and accelerations) acquired from real-life SHM systems are used to simulate the whole process of wireless transmission and test the algorithm's performance. The results demonstrate that (1) DCS-Laplace is an adaptative algorithm that can actively adapt to signals with various sparsity by adjusting the penalty term to achieve optimal performance; (2) compared with CS methods, DCS methods can effectively improve the reconstruction quality of multi-channel signals; (3) the Laplace method has advantages over the OMP method in terms of reconstruction performance and applicability, which is a better choice in SHM wireless signal transmission.
信号传输在结构健康监测（SHM）系统的日常运行中起着重要作用。在无线传感器网络中，传输损耗经常发生并威胁可靠的数据传递。大量的数据监测也导致在整个系统使用寿命期间信号传输和存储成本高昂。压缩感知（CS）为缓解这些问题提供了一个新视角。基于振动信号在频域中的稀疏性，CS可以仅从少量测量中重建出几乎完整的信号。这可以提高数据丢失时的鲁棒性，同时便于数据压缩以减少传输需求。分布式压缩感知（DCS）是从CS方法扩展而来的，它可以利用多个测量向量（MMV）之间的相关性来联合恢复具有相似稀疏模式的多通道信号，从而有效提高重建质量。本文构建了一个用于SHM中无线信号传输的综合DCS框架，将数据压缩和传输损耗过程结合在一起。与基本的DCS公式不同，所提出的框架不仅激活了通道间的相互相关性，还为单通道传输提供了灵活性和独立性。为了促进信号稀疏性，构建了一个使用拉普拉斯先验的分层贝叶斯模型，并进一步改进为用于大规模重建任务的快速迭代DCS - 拉普拉斯算法。从实际SHM系统中获取的振动信号（例如动态位移和加速度）用于模拟无线传输的全过程并测试算法性能。结果表明：（1）DCS - 拉普拉斯是一种自适应算法，它可以通过调整惩罚项来主动适应具有各种稀疏性的信号，以实现最佳性能；（2）与CS方法相比，DCS方法可以有效提高多通道信号的重建质量；（3）拉普拉斯方法在重建性能和适用性方面优于OMP方法，是SHM无线信号传输中更好的选择。
