A method to maximise the information obtained from low signal-to-noise acceleration data by optimising SSI-COV input parameters
A method to maximise the information obtained from low signal-to-noise acceleration data by optimising SSI-COV input parameters
复制标题
一种通过优化 SSI-COV 输入参数来最大化从低信噪比加速度数据中获取信息的方法
DOI：
10.1016/j.jsv.2023.118101
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发表时间：
2024
影响因子：
4.7
通讯作者：
O'Higgins C
中科院分区：
文献类型：
--
作者：
O'Higgins C
结构健康监测（SHM）主要针对较大的桥梁进行，并根据具体情况进行。这是由于一系列因素造成的，例如安装成本高以及安装和调试监控系统所需的工作量。让 SHM 系统在网络层面得到广泛采用变得更加可行的一种方法是减少所用传感器的数量和成本。然而，这需要权衡，因为低成本传感器通常具有较差的信噪比，并且传感器数量的减少需要仔细放置以最大化所获取的信息量。桥梁 SHM 最简单/最便宜的方法之一是长期跟踪桥梁频率以识别刚度变化。这项研究使用从五个在役桥梁收集的数据表明，用户定义的 SSI-COV 输入参数可以显着影响提取的固有频率的质量。因此，开发了一种新颖的方法来帮助选择 SSI-COV 方法中使用的输入。开发的方法还表明，确定的输入可以提取准确的固有频率，并且所有测试桥梁上的明显异常值最小。所开发的方法允许从低信噪比加速度数据中提取优质固有频率，这在进行基于频率的 SHM 时至关重要。
Structural Health Monitoring (SHM) has mainly been undertaken on larger bridges and on a case-by-case basis. This is due to a range of factors, such as the high installation costs and the effort required to install and commission the monitoring systems. One way in which SHM systems can become more feasible for widespread adoption at a network level is to reduce the number and cost of sensors used. However, this comes with a trade-off as low-cost sensors will typically have a worse signal-to-noise ratio and the reduced number of sensors requires careful placement to maximise the amount of information acquired. One of the simplest/cheapest methods of bridge SHM is long-term tracking of the bridge frequency to identify a change in stiffness. Using data collected from five in-service bridges, this research shows that the user-defined SSI-COV input parameters can significantly impact the quality of extracted natural frequencies. Consequently, a novel method is developed to aid in choosing the inputs used in the SSI-COV method. The method developed also showed that the determined inputs resulted in the extraction of accurate natural frequencies with minimal apparent outliers on all tested bridges. The developed method allows the extraction of quality natural frequencies from low signal-to-noise acceleration data which are vital when undertaking frequency-based SHM.
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DOI：
10.1117/12.434158
发表时间：
2001
期刊：
The Journal of the Acoustical Society of America
影响因子：
--
作者：
C. Farrar;H. Sohn;M. Fugate;Jerry J. Czarnecki
通讯作者：
Jerry J. Czarnecki
影响因子：
0.7
作者：
O'Higgins, C.
通讯作者：
O'Higgins, C.
DOI：
--
发表时间：
2019
期刊：
IOP Conference Series: Materials Science and Engineering
影响因子：
--
作者：
E. Zulkifli;D. R. Widarda
通讯作者：
D. R. Widarda
影响因子：
5.5
作者：
Brownjohn, J. M. W.;Magalhaes, Filipe;Cunha, Alvaro
通讯作者：
Cunha, Alvaro
从加速度数据中提取板载特征以进行活动识别
DOI：
--
发表时间：
2018
期刊：
European Conference/Workshop on Wireless Sensor Networks
影响因子：
--
作者：
Atis Elsts;Ryan McConville;Xenofon Fafoutis;N. Twomey;R. Piechocki;Raúl Santos;I. Craddock
通讯作者：
I. Craddock