Sensor clustering technique for practical structural monitoring and maintenance
Sensor clustering technique for practical structural monitoring and maintenance
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DOI：
10.12989/smm.2018.5.2.273
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发表时间：
2018-06-01
期刊：
影响因子：
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通讯作者：
Catbas, F. Necati
中科院分区：
文献类型：
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作者：
Celik, Ozan;Terrell, Thomas;Catbas, F. Necati
在这项研究中，提出了一个整体状态评估的损伤检测方法的调查。特别强调的是利用无线传感器更实用，更省时，更便宜，更安全的监测和最终的维护目的。无线传感器采用传感器巡回技术部署，以保持密集的传感器场，但需要较少的传感器。针对不同的传感器集群，采用时间序列分析方法ARX（Auto-Regressive Models with eXogeneous Input），对人工损伤及其位置进行了研究。该技术的性能进行了验证，通过使用从一个4跨桥梁型钢结构在受控的实验室环境中获得的数据集。其中，针对特定传感器集群的结构的自由响应振动数据由有线和无线传感器测量，并且每个传感器的加速度输出被用作ARX模型的输入以估计该集群的参考通道的响应。使用这两种数据类型，基于ARX的时间序列分析方法是有效的损伤检测和定位沿着的解释和结论。
In this study, an investigation of a damage detection methodology for global condition assessment is presented. A particular emphasis is put on the utilization of wireless sensors for more practical, less time consuming, less expensive and safer monitoring and eventually maintenance purposes. Wireless sensors are deployed with a sensor roving technique to maintain a dense sensor field yet requiring fewer sensors. The time series analysis method called ARX models (Auto-Regressive models with eXogeneous input) for different sensor clusters is implemented for the exploration of artificially induced damage and their locations. The performance of the technique is verified by making use of the data sets acquired from a 4-span bridge-type steel structure in a controlled laboratory environment. In that, the free response vibration data of the structure for a specific sensor cluster is measured by both wired and wireless sensors and the acceleration output of each sensor is used as an input to ARX model to estimate the response of the reference channel of that cluster. Using both data types, the ARX based time series analysis method is shown to be effective for damage detection and localization along with the interpretations and conclusions.