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首页> 外文期刊>Structural Control and Health Monitoring >Signal selection and analysis methodology of long‐term vibration data from the I‐35W St. Anthony Falls Bridge
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Signal selection and analysis methodology of long‐term vibration data from the I‐35W St. Anthony Falls Bridge

机译：I-35W圣安东尼瀑布桥的长期振动数据的信号选择和分析方法

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Large-scale, long-term structural health monitoring systems have become more feasible in recent years as the required data acquisition and analysis systems are more affordable to deploy. These long-term systems must process and store vast amounts of data without wasting computational power and storage capacity with redundant or poor quality data. While not a primary system for damage detection, large-scale, long-term vibration monitoring systems aim to leverage changes in the dynamic signature of a structure to assess global structural changes. Although the ability to continually collect vibration data at high rates exists, it is not always feasible to store all these data long term. As more long-term monitoring systems are deployed, efficient methods need to be developed to quickly and efficiently analyze large quantities of vibration data so that only the most pertinent information is archived. Previous researchers have used scheduled approaches, eg, taking data every hour, or triggered sensing systems. A monitoring system on the I-35W St. Anthony Falls Bridge, which crosses the Mississippi River in Minneapolis, Minnesota, has been collecting vibration and temperature data since the structure's opening in 2008. This provides a uniquely large data set to establish the characteristics of a good signal for output-only system identification to consistently and efficiently capture natural frequencies and mode shapes. To this end, a system identification routine using a novel signal selection approach and modal sorting routine that leverages NExT-ERA/DC is proposed to analyze this large data set. The resulting information allows long-term and temperature-based trends to be identified.
机译：近年来，随着所需数据采集和分析系统的部署更加经济实惠，大规模，长期的结构健康监控系统变得更加可行。这些长期系统必须处理和存储大量数据，而又不会因冗余或质量较差的数据而浪费计算能力和存储容量。大规模，长期的振动监测系统虽然不是主要的损伤检测系统，但其目的是利用结构动态特征的变化来评估整体结构变化。尽管存在以高速率连续收集振动数据的能力，但长期存储所有这些数据并不总是可行的。随着部署更多的长期监视系统，需要开发有效的方法来快速而有效地分析大量振动数据，以便仅归档最相关的信息。以前的研究人员已使用计划的方法，例如每小时获取数据或触发感应系统。自2008年该结构开放以来，横跨明尼苏达州明尼阿波利斯的密西西比河的I-35W圣安东尼瀑布桥上的监视系统就一直在收集振动和温度数据。这提供了独特的大型数据集，可确定该建筑物的特征。一个仅用于输出的系统识别的好信号，以一致且有效地捕获固有频率和模式形状。为此，提出了一种使用新型信号选择方法和利用NExT-ERA / DC的模态排序例程的系统识别例程，以分析此大数据集。结果信息可以确定长期趋势和基于温度的趋势。

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