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A new automated procedure of modal identification in operational conditions

机译:运行条件下模态识别的新自动化程序

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摘要

Structural Health Monitoring (SHM) strategies are aimed at the assessment of structural performance, using data acquired by sensing systems. Among the different available approaches, vibration-based methods - involving the automation of the modal parameter estimation (MPE) and modal tracking (MT) procedures - are receiving increasing attention. In the context of vibration-based monitoring, this paper presents an automated procedure of modal identification in operational conditions. The presented algorithms can be used to effectively manage the results obtained by any parametric identification method that involves the construction and the interpretation of stabilization diagrams. The implemented approach introduces improvements related to both the MPE and the MT tasks. The MPE procedure consists of three key steps aimed at: (1) filtering a high number of spurious poles in the stabilization diagram; (2) clustering the remaining poles that share same characteristics in term of modal parameters; (3) improving the accuracy of the modal parameter estimates. In the MT procedure the use of a simple statistical approach to define adaptive thresholds together with continuously updated dynamic reference list guarantee an efficient tracking of the most representative structural modes. The advantages obtained through the proposed procedures are exemplified using data continuously collected on the historic masonry tower of San Gottardo in Corte, located in the centre of Milan, Italy. In addition, the ability of the automated algorithms to identify contributions inherent to different vibration modes, even if they are characterized by closely-spaced frequencies and a low discriminant between mode shapes, will be described in details.
机译:结构健康监测(SHM)策略旨在使用传感系统获取的数据来评估结构性能。在各种可用的方法中,基于振动的方法-涉及模态参数估计(MPE)和模态跟踪(MT)程序的自动化-受到越来越多的关注。在基于振动的监测中,本文提出了一种在工况下进行模态识别的自动化程序。所提出的算法可用于有效管理通过涉及稳定图的构造和解释的任何参数识别方法获得的结果。实施的方法引入了与MPE和MT任务相关的改进。 MPE程序包括三个主要步骤,这些步骤旨在:(1)过滤稳定图中大量的虚假极点; (2)将模态参数相同的其余极点聚类; (3)提高模态参数估计的准确性。在MT程序中,使用简单的统计方法来定义自适应阈值以及不断更新的动态参考列表可确保对最具代表性的结构模式进行有效跟踪。通过在意大利米兰市中心的科尔特圣戈塔多(San Gottardo)历史悠久的砖石建筑中连续收集的数据,证明了通过建议的程序获得的优势。另外,将详细描述自动算法识别不同振动模式固有贡献的能力,即使它们以紧密间隔的频率和模式形状之间的低判别为特征。

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