节点文献

面向缩聚模型的结构损伤识别研究

Structural Damage Identification Using Reduced Order Finite-Element Model

  • 推荐 CAJ下载
  • PDF下载
  • 不支持迅雷等下载工具,请取消加速工具后下载。

【作者】 李蕊周丽

【Author】 LI Rui1,2,ZHOU Li1,2(1.MOE Key Laboratory of Structure Mechanics and Control for Aircraft,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China;2.Institute of Structures & Strength,Nanjing University ofAeronautics and Astronautics,Nanjing 210016,China)

【机构】 南京航空航天大学飞行器结构力学与控制教育部重点实验室南京航空航天大学结构与强度研究所

【摘要】 为了减少结构健康监测系统中传感器的数量,创新性地结合缩聚原理和自适应二次乘方和误差法提出了一种面向缩聚模型的结构损伤识别方法.根据模型缩聚原理对结构有限元模型进行了缩聚,依据缩聚后有限元模型、利用自适应二次乘方和误差法对结构参数进行了识别和追踪,从而实现了结构损伤的识别.对某一大型平面桁架结构进行了仿真,期间考虑了白噪声和地震波的影响;对某一悬臂梁结构进行了实验,以研究白噪声、正弦、拟El-Centro 3种激励下的6种情况.结果表明:所提方法只需使用少量的传感器便可精确地识别出结构中损伤的发生时刻和损伤的程度及位置,仿真结果的最大误差小于3%,实验结果的最大误差小于5%.

【Abstract】 In order to reduce the number of sensors in structure health monitoring systems,a structural damage identification method using the reduced order finite-element model was proposed by combining the adaptive quadratic sum-squares error approach.From the model reduction method,the structure finite element model is reduced.Moreover,using the reduced-order finite-element method and the adaptive quadratic sum-squares error approach,structural parameters can be identified and tracked,and then structural damages can be identified.A simulation test was performed on a large span plane truss considering white noise excitation and El-Centro earthquake excitation.The experimental tests were conducted on a scaled cantilever beam with the consideration of six cases under three excitations,including white noise,sinusoidal excitation,and El-Centro excitation.The simulation and experimental results show that the method proposed can effectively detect the structural damages including the damage location and severity with only a few sensors.

【基金】 航空基金资助项目(2008ZA52012);江苏省六大人才高峰资助项目(2010JZ004)
  • 【文献出处】 西安交通大学学报 ,Journal of Xi’an Jiaotong University , 编辑部邮箱 ,2011年09期
  • 【分类号】TP274
  • 【网络出版时间】2011-06-21 17:04
  • 【被引频次】1
  • 【下载频次】107
节点文献中: 

本文链接的文献网络图示:

本文的引文网络