Estimation of Joint Parameters Using Frequency-Based Substructuring Techniques

IF 2.3 4区 计算机科学 Q1 Engineering
Hye-Sook Jang, Jae-Hyoung An, Hee-Chang Eun
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引用次数: 0

Abstract

This study presents frequency-based substructuring (FBS) techniques and an identification method for predicting joint parameters. Two FBS techniques, FBS-1 and FBS-2, were derived by assuming pseudomasses at the joint nodes between adjacent substructures. It is estimated that the main reason for the discrepancy with the analytical FRFs is the difficulty in describing the low-frequency responses owing to the assumed pseudomasses of the substructures. Although the FRF curve based on the FBS-2 technique is very close to the analytical FRF curve up to the first resonance frequency, some inconsistencies occur thereafter. It is analyzed that the FRFs up to the first resonance frequency can be utilized for data expansion methods and system identification techniques. Paying attention to this result, this study also provides an identification method to estimate the joint parameters based on the FRF variation. Its validity is illustrated using a numerical example.
利用基于频率的子结构技术估算联合参数
本研究介绍了基于频率的子结构(FBS)技术和预测连接参数的识别方法。通过在相邻子结构之间的连接节点处假设伪质量,得出了两种 FBS 技术,即 FBS-1 和 FBS-2。据估计,与分析 FRF 存在差异的主要原因是,由于假定子结构为假体,因此难以描述低频响应。虽然基于 FBS-2 技术的 FRF 曲线在第一个共振频率之前与分析 FRF 曲线非常接近,但之后出现了一些不一致。据分析,第一个共振频率之前的 FRF 可以用于数据扩展方法和系统识别技术。考虑到这一结果,本研究还提供了一种基于 FRF 变化估算关节参数的识别方法。本研究通过一个数值示例说明了该方法的有效性。
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 Distributed Sensor Networks
International Journal of Distributed Sensor Networks Computer Science-Computer Networks and Communications
CiteScore
6.00
自引率
4.30%
发文量
94
审稿时长
11 weeks
期刊介绍: International Journal of Distributed Sensor Networks (IJDSN) is a JCR ranked, peer-reviewed, open access journal that focuses on applied research and applications of sensor networks. The goal of this journal is to provide a forum for the publication of important research contributions in developing high performance computing solutions to problems arising from the complexities of these sensor network systems. Articles highlight advances in uses of sensor network systems for solving computational tasks in manufacturing, engineering and environmental systems.
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