Department of Mechanical Engineering, University of Western Macedonia, Bakola and Sialvera, 50100 Kozani, Greece.
Sensors (Basel). 2022 Jul 6;22(14):5079. doi: 10.3390/s22145079.
The continuous development of new materials and larger and/or more complex structures drives the need for the development of more robust, accurate, and sensitive Structural Health Monitoring (SHM) techniques. In the present work, a novel vibration-based damage-detection method that contributes into the SHM field is presented using Metaheuristic algorithms coupled with optimal Finite Element Models that can effectively localize damage. The proposed damage-detection framework can be applied in any kind of detailed structural FE model, while requiring only the output information of the dynamic response of the structure. It can effectively localize damage in a structure by highlighting not only the affected part of the structure but also the specific damaged area inside the part. First, the optimal FE model of the healthy structure is developed using appropriate FE model updating techniques and experimental vibration measurements, simulating the undamaged condition. Next, the main goal of the proposed method is to create a damaged FE model that approximates the dynamic response of the damaged structure. To achieve this, a parametric area is inserted into the FE model, changing stiffness and mass to simulate the effect of the physical damage. This area is controlled by the metaheuristic optimization algorithm, which is embedded in the proposed damage-detection framework. On this specific implementation of the framework, the Particle Swarm Optimization (PSO) algorithm is selected which has been used for a wide variety of optimization problems in the past. On the PSO's search space, two parameters control the stiffness and mass of the damaged area while additional location parameters control the exact position of the damaged area through the FE model. For effective damage localization, the Transmittance Functions from acceleration measurements are used which have been shown to be sensitive to structural damage while requiring output-only information. Finally, with proper selection of the objective function, the error that arises from modeling a physical damage with a linear damaged FE model can be minimized, thus creating a more accurate prediction for the damaged location. The effectiveness of the proposed SHM method is demonstrated via two illustrative examples: a simulated small-scale model of a laboratory-tested vehicle-like structure and a real experimental CFRP composite beam structure. In order to check the robustness of the proposed method, two small damage scenarios are examined for each validation model and combined with random excitations.
新材料的不断发展和更大或更复杂的结构推动了对更强大、准确和敏感的结构健康监测 (SHM) 技术的需求。在本工作中，提出了一种使用元启发式算法结合最优有限元模型的新型基于振动的损伤检测方法，该方法可有效定位损伤。所提出的损伤检测框架可应用于任何类型的详细结构有限元模型，而仅需要结构动态响应的输出信息。它可以通过突出显示结构的受影响部分以及部分内部的特定损坏区域，有效地定位结构中的损伤。首先，使用适当的有限元模型更新技术和实验振动测量来开发健康结构的最优有限元模型，模拟无损伤状态。接下来，所提出方法的主要目标是创建一个接近损伤结构动态响应的损伤有限元模型。为了实现这一目标，在有限元模型中插入一个参数区域，通过改变刚度和质量来模拟物理损伤的影响。该区域由元启发式优化算法控制，该算法嵌入在所提出的损伤检测框架中。在该框架的特定实现中，选择了粒子群优化 (PSO) 算法，该算法过去已用于各种优化问题。在 PSO 的搜索空间中，两个参数控制损伤区域的刚度和质量，而附加的位置参数通过有限元模型控制损伤区域的确切位置。为了进行有效的损伤定位，使用加速度测量的传递函数，该函数已被证明对结构损伤敏感，同时仅需要输出信息。最后，通过适当选择目标函数，可以最小化用线性损伤有限元模型模拟物理损伤所产生的误差，从而对损伤位置进行更准确的预测。通过两个说明性示例验证了所提出的 SHM 方法的有效性：一个实验室测试的车辆样结构的模拟小尺度模型和一个真实的实验 CFRP 复合材料梁结构。为了检查所提出方法的鲁棒性，对每个验证模型检查了两个小损伤情况，并与随机激励相结合。
