Assessment of sub-Nyquist deterministic and random data sampling techniques for operational modal analysis
Assessment of sub-Nyquist deterministic and random data sampling techniques for operational modal analysis
复制标题
DOI：
10.1177/1475921717725029
复制
发表时间：
2017-08
期刊：
影响因子：
--
通讯作者：
K. Gkoktsi;A. Giaralis
中科院分区：
文献类型：
--
作者：
K. Gkoktsi;A. Giaralis
本文数值评估了两种不同的频谱估计方法的潜力，支持非均匀的时间数据采样在次奈奎斯特平均速率（即低于奈奎斯特频率），以减少数据传输有效载荷的无线传感器网络的土木工程结构的操作模态分析。这种考虑放宽了无线传感器网络中的传输带宽限制，并延长了传感器电池寿命，因为无线传输是最耗能的传感器上操作。这两种方法都假设获取子奈奎斯特结构响应加速度测量值并传输到基站，而无需传感器上的处理。响应加速度功率谱密度矩阵估计直接从次奈奎斯特测量，和结构振型提取使用频域分解算法。第一种方法依赖于压缩感测理论来处理亚奈奎斯特随机采样数据，假设加速度信号在频域中是稀疏的/可压缩的（即，具有少量的具有显著幅度的傅立叶系数）。第二种方法是基于功率谱盲采样技术，考虑周期性确定性次奈奎斯特“多陪集”采样和处理的加速度信号作为广义平稳随机过程，而不构成任何稀疏条件。模态保证准则是通过量化的质量的模态振型由两种方法得出在不同的次奈奎斯特压缩率使用计算机生成的信号的不同稀疏性和现场记录的固定数据有关的立交桥在苏黎世，瑞士。结果表明，对于一个给定的压缩比，基于压缩感知的方法的性能是间接影响信号的稀疏性，而基于功率谱盲采样的方法实现的模态保证标准>0.96独立于信号的稀疏性的压缩比低至22%的奈奎斯特率。它的结论是，基于功率谱盲采样的方法有效地降低了无线传感器网络的数据传输要求的操作模态分析，而不受信号稀疏性的限制，不需要先验假设或信号稀疏性的知识。
This article assesses numerically the potential of two different spectral estimation approaches supporting non-uniform-in-time data sampling at sub-Nyquist average rates (i.e. below the Nyquist frequency) to reduce data transmission payloads in wireless sensor networks for operational modal analysis of civil engineering structures. This consideration relaxes transmission bandwidth constraints in wireless sensor networks and prolongs sensor battery life since wireless transmission is the most energy-hungry on-sensor operation. Both the approaches assume acquisition of sub-Nyquist structural response acceleration measurements and transmission to a base station without on-sensor processing. The response acceleration power spectral density matrix is estimated directly from the sub-Nyquist measurements, and structural mode shapes are extracted using the frequency-domain decomposition algorithm. The first approach relies on the compressive sensing theory to treat sub-Nyquist randomly sampled data assuming that the acceleration signals are sparse/compressible in the frequency domain (i.e. have a small number of Fourier coefficients with significant magnitude). The second approach is based on a power spectrum blind sampling technique considering periodic deterministic sub-Nyquist “multi-coset” sampling and treating the acceleration signals as wide-sense stationary stochastic processes without posing any sparsity conditions. The modal assurance criterion is adopted to quantify the quality of mode shapes derived by the two approaches at different sub-Nyquist compression rates using computer-generated signals of different sparsity and field-recorded stationary data pertaining to an overpass in Zurich, Switzerland. It is shown that for a given compression rate, the performance of the compressive sensing–based approach is detrimentally affected by signal sparsity, while the power spectrum blind sampling–based approach achieves modal assurance criterion >0.96 independently of signal sparsity for compression ratios as low as 22% the Nyquist rate. It is concluded that the power spectrum blind sampling–based approach reduces effectively data transmission requirements in wireless sensor networks for operational modal analysis, without being limited by signal sparsity and without requiring a priori assumptions or knowledge of signal sparsity.