Parameter selection for model updating with global sensitivity analysis

被引：65

作者：

Yuan, Zhaoxu ^{[1
]}

Liang, Peng ^{[2
]}

Silva, Tiago ^{[3
]}

Yu, Kaiping ^{[1
]}

Mottershead, John E. ^{[2
]}

机构：

[1] Harbin Inst Technol, Dept Astronaut Sci & Mech, POB 304,92 West Dazhi St, Harbin 150001, Heilongjiang, Peoples R China

[2] Univ Liverpool, Dept Mech Mat & Aerosp Engn, Liverpool L69 3GH, Merseyside, England

[3] Univ Nova Lisboa, UNIDEMI, Dept Mech & Ind Engn, Campus Caparica, P-2829516 Caparica, Portugal

来源：

MECHANICAL SYSTEMS AND SIGNAL PROCESSING | 2019年 / 115卷

基金：

美国国家科学基金会;

关键词：

Model updating; Parameter selection; Uncertainty; Global sensitivity; DAMAGE IDENTIFICATION; UNCERTAINTY; INDEXES; QUANTIFICATION; DESIGN;

D O I：

10.1016/j.ymssp.2018.05.048

中图分类号：

TH [机械、仪表工业];

学科分类号：

0802 ;

摘要：

The problem of selecting parameters for stochastic model updating is one that has been studied for decades, yet no method exists that guarantees the 'correct' choice. In this paper, a method is formulated based on global sensitivity analysis using a new evaluation function and a composite sensitivity index that discriminates explicitly between sets of parameters with correctly-modelled and erroneous statistics. The method is applied successfully to simulated data for a pin-jointed truss structure model in two studies, for the cases of independent and correlated parameters respectively. Finally, experimental validation of the method is carried out on a frame structure with uncertainty in the position of two masses. The statistics of mass positions are confirmed by the proposed method to be correctly modelled using a Kriging surrogate. (C) 2018 Elsevier Ltd. All rights reserved.

引用

页码：483 / 496

页数：14

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