Compressive sensing of wireless sensors based on group sparse optimization for structural health monitoring
Compressive sensing of wireless sensors based on group sparse optimization for structural health monitoring
复制标题
基于组稀疏优化的无线传感器压缩感知结构健康监测
DOI：
10.1177/1475921717721457
复制
发表时间：
2018-07-01
影响因子：
6.6
通讯作者：
Li, Hui
中科院分区：
文献类型：
--
作者：
Bao, Yuequan;Shi, Zuoqiang;Li, Hui
大多数民用基础设施的振动信号具有稀疏特征（即只有少数模态对结构的振动有贡献）。因此，振动数据通常具有稀疏表示。此外，放置在结构不同位置的传感器测得的振动数据在频域上具有几乎相同的稀疏结构。基于结构振动数据的群稀疏性，提出了一种基于压缩感知的无线传感器群稀疏优化算法。与奈奎斯特抽样定理不同的是，该方法首先根据压缩感知理论采用非均匀低速率随机抽样方法获取数据。然后，我们开发了组稀疏优化算法，从不完整的测量中重建原始数据。通过对厦门海沧大桥无线传感器的现场测试，验证了该方法的有效性。结果表明，采用群稀疏优化方法对多传感器数据进行重构，可以获得比单传感器数据更小的重构误差。即使仅使用10%的随机抽样数据，也可以使用组稀疏优化方法重建原始数据，重建误差很小。此外，还可以从重构数据中识别模态参数，识别误差较小。
Vibration signals of most civil infrastructures have sparse characteristics (i.e. only a few modes contribute to the vibration of the structures). Therefore, the vibration data usually have sparse representation. Additionally, the vibration data measured by the sensors placed on different locations of structure have almost the same sparse structure in the frequency domain. On basis of the group sparsity of the structural vibration data, we proposed a group sparse optimization algorithm based on compressive sensing for wireless sensors. Different from the Nyquist sampling theorem, the data are first acquired by a nonuniform low-rate random sampling method according to compressive sensing theory. We then developed the group sparse optimization algorithm to reconstruct the original data from incomplete measurements. By conducting a field test on Xiamen Haicang Bridge with wireless sensors, we illustrate the effectiveness of the proposed approach. The results show that smaller reconstruction errors can be achieved using data from multiple sensors with the group sparse optimization method than using data from only single sensor. Even using only 10% random sampling data, the original data can be reconstructed using the group sparse optimization method with a small reconstruction error. In addition, the modal parameters can also be identified from the reconstruction data with small identification errors.