One important topic for structural health monitoring is to achieve accurate damage detection with a small number of noisy sensors and without the requirement of a high-fidelity finite element model. This article adopts the Bayesian probabilistic approach combined with a perturbation model using responses at a few vibration nodes for damage monitoring. First, the node displacement, or the response at vibration node, is adopted in this study for real-time damage assessment with a relatively small number of sensors. Then, the construction method of the node displacement response curves based on the perturbation model is proposed to replace the expensive finite element model. After that, a Bayesian framework integrated the node displacement measurement and the response curves are adopted to acquire the probability distribution of the damage parameter. In this article, the accuracy of the node displacement–based Bayesian framework with the perturbation method is evaluated. The proposed method is applied to a supporting structure of a sailplane under different noise levels.

结构健康监测的一个重要主题是使用少量的噪声传感器实现精确的损伤检测，而无需使用高保真有限元模型。本文采用贝叶斯概率方法结合微扰模型，该模型使用几个振动节点处的响应进行损伤监测。首先，本研究采用节点位移或振动节点的响应来通过相对较少的传感器进行实时损伤评估。然后，提出了基于扰动模型的节点位移响应曲线构造方法，以代替昂贵的有限元模型。然后，采用贝叶斯框架，结合节点位移测量和响应曲线，获取损伤参数的概率分布。在本文中，使用摄动方法评估了基于节点位移的贝叶斯框架的准确性。所提出的方法适用于不同噪声水平下的帆板支撑结构。