Data correlation analysis for optimal sensor placement using a bond energy algorithm

被引:30
作者
Lu, Wei [1 ]
Wen, Runfa [1 ]
Teng, Jun [1 ,2 ]
Li, Xiaoling [1 ]
Li, Chao [1 ]
机构
[1] Harbin Inst Technol, Shenzhen Grad Sch, Dept Civil & Environm Engn, Xili Univ Town HIT Campus, Shenzhen 518055, Peoples R China
[2] Fujian Univ Technol, Coll Civil Engn, 3 Xueyuan Rd Univ Town, Fuzhou 350108, Peoples R China
基金
美国国家科学基金会;
关键词
Structural health monitoring; Optimal sensor placement; Data correlation; Bond energy algorithm; IDENTIFICATION; RECONSTRUCTION; STIFFNESS; SET;
D O I
10.1016/j.measurement.2016.05.089
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Optimal sensor placement is one of the crucial and fundamental factors for constructing a cost-effective structural health monitoring system and is related to the effective evaluation of the state of the structure. Structural responses are correlated to some extent, as the structural behavior is continuous. Based on the above two considerations, the question arises of how to obtain the maximum amount of information for understanding the structure using measurements from limited sensors and not be limited to direct monitoring at the placements where the limited sensors are located. Data correlation analysis for optimal sensor placement is proposed using a bond energy algorithm, in which the objectives, such as structural response evaluation covering the maximum structural responses using measurements from sensors located at the optimal placements, are taken into account. The data correlation analysis is conducted for the structural responses, and the correlation matrix is established. Furthermore, the optimal sensor placements and the correlation of the responses at element locations can be determined using the bond energy algorithm. A Schwedler single-layer spherical lattice dome-like structure, which is a common large space steel structure, is used to simulate the structural responses and verify the effectiveness of the proposed method by discussion of different scenarios of parameter selection. (C) 2016 Elsevier Ltd. All rights reserved.
引用
收藏
页码:509 / 518
页数:10
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