Output-Only Vibration-Based Monitoring of Civil Infrastructure via Sub-Nyquist/Compressive Measurements Supporting Reduced Wireless Data Transmission
Output-Only Vibration-Based Monitoring of Civil Infrastructure via Sub-Nyquist/Compressive Measurements Supporting Reduced Wireless Data Transmission
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DOI：
10.3389/fbuil.2019.00111
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发表时间：
2019-09
影响因子：
3
通讯作者：
K. Gkoktsi;A. Giaralis;R. Klis;V. Dertimanis;E. Chatzi
中科院分区：
文献类型：
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作者：
K. Gkoktsi;A. Giaralis;R. Klis;V. Dertimanis;E. Chatzi
考虑无线加速度传感器是非常有前途的成本效益的输出只有系统识别的背景下，大型土木结构的操作模态分析（OMA），因为它们减轻了布线的需要。然而，使用无线单元的OMA的实际监测实现遭受与密集采样的加速度时间序列的无线传输相关的许多缺点，包括感测节点的能量自维持性。在这项工作中，最近提出的两种方法，输出只有模态识别解决上述问题，通过平衡监测精度与数据传输成本进行了比较研究和数值评估，使用现场记录的加速度数据集从两个不同的结构：（i）一个运行在岸上的风力涡轮机，（ii）一个开放的交通公路桥。一种方法利用非均匀的时间确定性多陪集采样在亚奈奎斯特率捕获结构响应加速度时间序列的环境激励下，假设平稳信号条件。在该方法中，功率谱盲采样技术被用来估计响应加速度功率谱密度矩阵从低速率采样的测量，并耦合到OMA的频域分解方法。另一种是频谱-时间压缩感知方法，该方法通过在时域中从亚奈奎斯特非均匀时间随机采样测量中重构时间序列来恢复响应加速度信号。通过智能传感器操作和传感器/服务器通信，利用谱域中信号结构的先验知识。所考虑的方法的优点和局限性进行了讨论，并通过处理现场记录的数据集的不同水平的信号压缩，并通过估计电池寿命增益在一个单一的传感器实现减少数据传输。它的结论是，这两种方法是很容易适用于OMA的大型结构，并可以互补地使用，这取决于任何特定的加速度监测活动的要求：时间序列提取进一步询问与单独的模态特性估计。
The consideration of wireless acceleration sensors is highly promising for cost-effective output-only system identification in the context of operational modal analysis (OMA) of large-scale civil structures as they alleviate the need for wiring. However, practical monitoring implementations for OMA using wireless units suffer a number of drawbacks related to wireless transmission of densely sampled acceleration time-series including the energy self-sustainability of the sensing nodes. In this work, two recently proposed approaches for output-only modal identification addressing the above issues through balancing monitoring accuracy with data transmission costs are comparatively studied and numerically assessed using field recorded acceleration datasets from two different structures: (i) an operating on-shore wind turbine, (ii) an open to traffic highway bridge. One approach utilizes non-uniform-in-time deterministic multi-coset sampling at sub-Nyquist rates to capture structural response acceleration time-series under ambient excitation assuming stationary signal conditions. In this approach, a power spectrum blind sampling technique is used to estimate the response acceleration power spectral density matrix from the low-rate sampled measurements and is coupled with the Frequency Domain Decomposition method of OMA. The other is a spectro-temporal compressive sensing approach which recovers response acceleration signals through time-series reconstruction in the time domain from sub-Nyquist non-uniform-in-time randomly sampled measurements. Prior knowledge of signal structure in the spectral domain is exploited through smart on-sensor operations and sensor/server communication. The benefits and limitations of the considered approaches are discussed and demonstrated by processing the field recorded datasets for different levels of signal compression and by estimating battery lifetime gains at a single sensor achieved by reduced data transmission. It is concluded that the two approaches are readily applicable in OMA of large-scale structures and can be used complementarily depending on the requirements of any particular acceleration monitoring campaign: time-series extraction for further interrogation vs. solely modal properties estimation.