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A new automated procedure of modal identification in operational conditions

机译:操作条件下模态识别的新自动化程序

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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得的数据评估结构性能。在不同的可用方法中,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 - 正在接受不断的关注。在基于振动的监测的背景下,本文介绍了操作条件下模态识别的自动化过程。所提出的算法可用于有效地管理通过任何参数识别方法获得的结果,该方法涉及构造和稳定图的解释。实现的方法介绍了与MPE和MT任务相关的改进。 MPE程序由三个关键步骤组成:(1)在稳定图中过滤大量的杂散极; (2)在模态参数中聚类共享相同特征的剩余杆; (3)提高模态参数估计的准确性。在MT过程中,使用简单的统计方法来定义自适应阈值以及连续更新的动态参考列表保证最有效地跟踪最多代表性的结构模式。通过所提出的程序获得的优点是使用持续收集的数据在Corte的San Gottardo的历史悠久的砌体塔上持续收集的数据,位于意大利米兰的中心。另外,将详细描述,即使它们的特征在于,将详细描述自动化算法的能力,即使它们的特征在于模式形状之间是紧密间隔的频率和低判别的特征。

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