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首页> 外文期刊>Journal of bridge engineering >Decentralized Modal Identification of a Pony Truss Pedestrian Bridge Using Wireless Sensors
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Decentralized Modal Identification of a Pony Truss Pedestrian Bridge Using Wireless Sensors

机译:使用无线传感器的小马桁架人行天桥的分散模态识别

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摘要

Most of the vibration-based ambient modal identification methods in the literature are structured to process vibration data collected from a dense array of sensors centrally to yield modal information. For large systems, for example bridges, one of the main disadvantages of such a centralized architecture is the cost of dense instrumentation, predominantly consisting of the sensors themselves, the data acquisition system, and the associated cabling. Recent advances in wireless smart sensors have addressed the issue of sensor cost to some extent; however, most of the algorithms-with the exception of very few-still retain an essentially centralized architecture. To harness the full potential of decentralized implementation, the authors have developed a new class of algorithms exploiting the concepts of sparsity (using wavelet transforms) within the framework of blind source separation. The problem of identification is cast within the framework of underdetermined blind source separation invoking transformations of measurements to the wavelet domain resulting in a sparse representation. Although the details of these decentralized algorithms have been discussed in other articles, in this paper, for the first time, these algorithms are studied experimentally on a full-scale structure using wireless sensors. In a truly decentralized implementation, only two sensors are roved along the length of a pedestrian bridge, and the performance of the proposed algorithms is studied in detail. A pedestrian bridge located in Montreal, Quebec, Canada, is chosen primarily to highlight the methodology used to address modal identification under low-sensor density and for pedestrian loading. Issues arising from several modes being excited on this bridge and the presence of narrowband pedestrian excitations are addressed. The accuracy of modal identification that is achieved using the proposed decentralized algorithms is compared with the results obtained from their centralized counterparts.
机译:文献中大多数基于振动的环境模态识别方法都被构造为处理从密集的传感器阵列集中收集的振动数据,以产生模态信息。对于大型系统(例如桥梁),这种集中式体系结构的主要缺点之一是昂贵的仪表成本,该仪表主要由传感器本身,数据采集系统和相关的电缆组成。无线智能传感器的最新进展已在一定程度上解决了传感器成本的问题。但是,大多数算法(除了极少数的算法除外)仍然保留了本质上集中的架构。为了充分利用分散实施的潜力,作者开发了一种新型算法,在盲源分离框架内利用稀疏性概念(使用小波变换)。识别问题是在不确定的盲源分离框架内进行的,该框架需要将测量转换为小波域,从而导致稀疏表示。尽管这些分散算法的细节已在其他文章中进行了讨论,但本文还是首次使用无线传感器在全尺寸结构上对这些算法进行了实验研究。在真正的分散式实施中,沿着人行天桥的长度只巡回了两个传感器,并详细研究了所提出算法的性能。选择位于加拿大魁北克省蒙特利尔的人行天桥主要是为了突出显示用于解决低传感器密度下的模式识别以及行人负载的方法。解决了由在该桥上激发几种模式引起的问题以及窄带行人激发的存在。使用所提出的分散算法实现的模态识别的准确性与从其集中式对应项获得的结果进行了比较。

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