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A Bayesian probabilistic approach for damage identification in plate structures using responses at vibration nodes
Mechanical Systems and Signal Processing ( IF 7.9 ) Pub Date : 2021-01-01 , DOI: 10.1016/j.ymssp.2020.106998
Tianxiang Huang , Kai-Uwe Schröder

Abstract Structural health monitoring for plate structures is important since they are essential structural components in many applications. One interesting topic for structural health monitoring methods is to achieve accurate damage detection with a small number of sensors and without the requirement of a high-fidelity finite element model. Traditional damage detection techniques for plates need many sensors to be distributed on the surface of the plate. This paper adopts dynamic responses at a few vibration nodal points combined with a Bayesian probabilistic approach for damage identification in plate structures. Vibrational amplitudes at nodal points, also referred as node displacement or NODIS, have the potential to achieve real-time damage assessment with a relatively small number of sensors. Thus, they can serve as efficient structural damage indicators. Despite these advantages, this method has not been applied to plate-type structures. This paper proposes a vibration-based SHM method for plates that is suitable for real-time monitoring, requires a small number of industrial sensors, does not rely on a high-fidelity FE model and can be applied for damage assessment of location and severity. In the Bayesian framework, an efficient perturbation-based surrogate model is derived for plate structures to replace the expensive FE model. The accuracy of the perturbation-based surrogate model is investigated and compared with FE results. Then, this paper evaluates the performance of the NODIS-based Bayesian framework with the perturbation method by comparing it with FE results. At last, the proposed method is applied to a carbon fiber reinforced polymer sandwich structure with different grinding depths.

中文翻译:

使用振动节点响应的用于板结构损伤识别的贝叶斯概率方法

摘要 板结构的结构健康监测很重要,因为它们是许多应用中必不可少的结构部件。结构健康监测方法的一个有趣主题是使用少量传感器实现准确的损坏检测,而无需高保真有限元模型。传统的板材损伤检测技术需要在板材表面分布多个传感器。本文采用几个振动节点的动态响应结合贝叶斯概率方法进行板结构损伤识别。节点处的振动幅度,也称为节点位移或 NODIS,有可能使用相对较少的传感器实现实时损坏评估。因此,它们可以作为有效的结构损坏指标。尽管有这些优点,但这种方法还没有应用于板式结构。本文提出了一种适用于实时监测、需要少量工业传感器、不依赖于高保真有限元模型、可用于位置和严重程度的损坏评估的基于振动的板的 SHM 方法。在贝叶斯框架中,为板结构导出了一种基于微扰的有效替代模型,以取代昂贵的有限元模型。研究了基于扰动的替代模型的准确性,并与有限元结果进行了比较。然后,本文通过与有限元结果进行比较,用扰动方法评估基于 NODIS 的贝叶斯框架的性能。最后,
更新日期:2021-01-01
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