Optimal Sensor Placement for a Truss Structure Using Particle Swarm Optimisation Algorithm

IF 0.8 4区工程技术 Q4 ACOUSTICS

International Journal of Acoustics and Vibration Pub Date : 2017-12-01 DOI:10.20855/IJAV.2017.22.4489

Wu Xiaohong, Zhao Jianhua, Z. Ling, Sun Qing

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引用次数: 6

Abstract

The problem of placing sensors plays a significant role in the domains of structural health monitoring (SHM) applications and parameter estimation in structural dynamics. In this paper, the particle swarm optimisation (PSO) algorithm is introduced firstly and utilised to place sensors optimally on a truss structure for the purpose of modal identification. Then, two different types of fitness functions are constructed as to be the optimal criteria, which are based on modal assurance criterion (MAC) and maximising measures of the observability gramian matrix, respectively. The former one is mainly used to distinguish between two sets of mode shapes with the aim of maximising the off-diagonal elements in MAC matrix, and the latter one ensures proper observability of the structure. Finally, a truss structure model is considered as an example to demonstrate the efficiency and validity of the proposed method. Numerical results show that the two approaches using the PSO algorithm can find the optimal location of sensors successfully and identify the modal frequencies of the truss structure accurately by acceleration FRF method.

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基于粒子群优化算法的特拉斯结构传感器优化布置

传感器的放置问题在结构健康监测（SHM）应用和结构动力学参数估计领域发挥着重要作用。本文首先介绍了粒子群优化算法（PSO），并将其应用于特拉斯结构上传感器的优化布置，以实现模态识别。然后，构造了两种不同类型的适应度函数作为最优准则，它们分别基于模态保证准则（MAC）和可观察性gramian矩阵的最大化测度。前者主要用于区分两组振型，目的是最大化MAC矩阵中的非对角元素，而后者确保结构的适当可观察性。最后以特拉斯结构模型为例验证了该方法的有效性和有效性。数值计算结果表明，采用PSO算法的两种方法可以成功地找到传感器的最佳位置，并通过加速度FRF方法准确地识别特拉斯结构的模态频率。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Acoustics and Vibration ACOUSTICS-ENGINEERING, MECHANICAL

CiteScore

1.60

自引率

10.00%

发文量

审稿时长

12 months

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