Model-Based Damage Localization Using the Particle Swarm Optimization Algorithm and Dynamic Time Wrapping for Pattern Recreation

基于粒子群优化算法和动态时间缠绕的损伤定位模式重构模型

阅读:1

Abstract

Vibration-based damage detection methods are a subcategory of Structural Health Monitoring (SHM) methods that rely on the fact that structural damage will affect the dynamic characteristic of a structure. The presented methodology uses Finite Element Models coupled with a metaheuristic optimization algorithm in order to locate the damage in a structure. The search domains of the optimization algorithm are the variables that control a parametric area, which is inserted into the FE model. During the optimization procedure, this area changes location, stiffness, and mass to simulate the effect of the physical damage. The final output is a damaged FE model which can approximate the dynamic response of the damaged structure and indicate the damaged area. For the current implementation of this Damage Detection Framework, the Particle Swarm Optimization algorithm is used. As an effective metric of the comparison between the FE model and the experimental structure, Transmittance Functions (TF) are used that require output only acceleration signals. As with most model-based methods, a common concern is the modeling error and how this can be surpassed. For this reason, the Dynamic Time Wrapping (DTW) algorithm is applied. When damage occurs in a structure it creates some differences between the Transmittance Functions (TF) of the healthy and the damaged state. With the use of DTW, the damaged pattern is recreated around the TF of the FE model, while creating the same differences and, thus, minimizing the modeling error. The effectiveness of the proposed methodology is tested on a small truss structure that consists of Carbon-Fiber Reinforced Polymer (CFRP) filament wound beams and aluminum connectors, where four cases are examined with the damage to be located on the composite material.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。