基于谱元法和北方苍鹰算法的振动损伤检测  

Vibration Damage Detection Based on the Spectral Element Method and Northern Goshawk Optimizer Algorithm

作　　者：周运来 姚峰 白春玉[2] 李凯翔[2] 朱胜阳[3] Zhou Yunlai;Yao Feng;Bai Chunyu;Li Kaixiang;Zhu Shengyang(State Key Laboratory for Strength and V ibration of Mechanical Structures,School of Aerospace Engineering,Xi'an Jiaotong University,Xi'an 710049,China;National Key Laboratory of Strength and Structural Integrity,Aircraft Strength Research Institute of China,Xi'an 710065,China;State Key Laboratory of Rail Transit Vehicle System,Southwest Jiaotong University,Chengdu 611756,China)

机构地区：[1]西安交通大学航天航空学院复杂服役环境重大装备结构强度与寿命全国重点实验室,陕西西安710049 [2]中国飞机强度研究所强度与结构完整性全国重点实验室,陕西西安710065 [3]西南交通大学轨道交通运载系统全国重点实验室,四川成都611756

出　　处：《华东交通大学学报》2024年第5期29-38,共10页Journal of East China Jiaotong University

基　　金：陕西省自然科学基础研究计划-面上项目(2023-JC-YB-007)。

摘　　要：【目的】为了解决结构损伤检测中有限元法建模不精确和计算成本高的问题,提出了一种结合谱元法与北方苍鹰优化算法(NGO)的结构健康监测(SHM)技术。【方法】首先,采用谱元法建立结构的频响函数,并应用于结构的损伤定位与损伤检测目标函数的构造,将损伤检测划分为两阶段问题,以降低算法优化维度和检测复杂性。其次,引入北方苍鹰优化算法(NGO),对目标函数进行优化求解。最后,以平面桁架结构和ASCE Benchmark结构为研究对象,利用NGO、粒子群优化(PSO)和蚁狮优化(ALO)算法对其各种损伤工况进行损伤检测性能对比。【结果】结果表明,在低维度和简单结构中,NGO,PSO和ALO算法均表现出良好的求解能力;但在高维度和大型复杂结构中,NGO相较于PSO和ALO算法具有更高的损伤检测能力和鲁棒性。【结论】改进后的方法提高了损伤检测数值建模的精度。【Objective】To address the issues of imprecise modeling and high computational cost in structural dam-age detection using the finite element method(FEM),this study proposes a structural health monitoring(SHM)technique that combines the spectral element method(SEM)with the Northern Goshawk Optimization(NGO)algo-rithm.【Method】Firstly,the spectral element method was used to establish the frequency response function of the structure,which was then applied to construct the objective function for damage localization and detection.This ap-proach divided the damage detection problem into two stages,reducing the optimization dimension and complexity.Secondly,NGO algorithm was introduced to optimize and solve the objective function.Finally,planar truss struc-ture and ASCE Benchmark Structure were used as case studies to compare the damage detection performance of NGO,Particle Swarm Optimization(PSO),and Ant Lion Optimization(ALO)algorithms under various damage cas-es.【Result】The results show that for low-dimensional and simple structures,NGO,PSO,and ALO algorithms all exhibit good solving capabilities.However,for high-dimensional and large complex structures,NGO demonstrates superior damage detection capability and robustness compared to PSO and ALO.【Conclusion】The improved method enhances the accuracy of numerical modeling in damage detection.

关 键 词：谱元法 损伤检测 NGO ASCE Benchmark Structure 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]