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首页> 外文期刊>International Journal of Sustainable Materials and Structural Systems >Towards real-time health monitoring of structural systems via recursive Bayesian filtering and reduced order modelling
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Towards real-time health monitoring of structural systems via recursive Bayesian filtering and reduced order modelling

机译：通过递归贝叶斯滤波和降阶建模实现对结构系统的实时健康监控

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摘要

A method for the structural health monitoring (SHM) of compliant, thin plates is discussed. With a specific focus on lightweight composite structures, a proposal for the optimal deployment of a network of surface-mounted inertial micro-sensors (MEMS) is reviewed. Allowing for the measurements gathered through the sensor network as (partial) observations of the structural state, a hybrid Kalman-particle filtering scheme is adopted to track the response of the plate to the external excitations and simultaneously identify unknown model parameters, among which damage or integrity indices. To move towards a real-time SHM procedure, the mentioned tracking and identification tasks are performed on a reduced-order model of the structure, continuously tuned after damage inception by a further Kalman filter. Results are reported for the exemplary case of a square plate, simply supported along its boundary, loaded by a concentrated force at its centre and developing a uniform damage in regions of its mid-plane area.
机译：讨论了一种用于顺应性薄板的结构健康监测（SHM）的方法。特别关注轻型复合材料结构，对用于表面安装惯性微传感器（MEMS）网络的最佳部署的提案进行了审查。考虑到通过传感器网络收集的测量结果（作为结构状态的（部分）观察结果），采用了混合卡尔曼粒子滤波方案来跟踪板对外部激励的响应并同时识别未知的模型参数，其中包括损伤或完整性指标。为了迈向实时SHM程序，上述跟踪和识别任务是在结构的降阶模型上执行的，并在损伤开始后通过另一个卡尔曼滤波器对其进行连续调整。报告了示例性情况的结果，该示例是方形板沿其边界简单地支撑，在其中心受到集中力加载并在其中平面区域产生均匀损坏的情况。

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