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Load dependent sensor placement method: Theory and experimental validation

delete2012-08-01
delete42
PRE
AI
D
Dongsheng Li *
H
Hong‐Nan Li
C
Claus‐Peter Fritzen
DOI:10.1016/j.ymssp.2012.04.014delete
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摘要

摘要

En 中文
A structural health monitoring system consists of permanently installed sensors to collect structural information, and these sensors are required to be placed at 'good' positions for damage identification. Conventional sensor placement methods make use of dynamic characteristics of a structure, i.e., mode shapes and natural frequencies, to determine optimal sensor positions. However, these methods do not take into account actual loading conditions and structural responses. In fact, participation degree of interested mode shapes in structural responses is of importance in both sensor placement and subsequent modal identification. In the work, a novel load dependent sensor placement method is developed by taking into consideration both structural dynamic characteristics and actual loading conditions. The objective of the proposed method is to achieve a nearly global unbiased estimate of modal coordinates, and consequently best modal and damage identification. It selects optimal sensor positions by subspace approximation of actual structural responses with the linear space spanned by interested mode shapes. Experiments are conducted on a six-story truss structure to validate the proposed load dependent sensor placement method. It is found that changing load conditions have to be accounted for when the issue of sensor placement is seriously examined. Furthermore, experiments have shown that better mode shape identification can be achieved at sensor positions chosen by the proposed novel load dependent sensor placement method than by conventional techniques. (C) 2012 Elsevier Ltd. All rights reserved.
Keyword:
Sensor placement
Structural health monitoring
Load dependent sensor placement method
Effective Independence
Modal identification
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期刊

Mechanical Systems and Signal Processing 封面图
Mechanical Systems and Signal Processing
IF:
8.9
论文数:
1.3W
被引数:
6.6W

机构

D
Dalian University of Technology
学者数:
6.0W
论文数: 4.4W
被引数: 5.5W
U
Universitat Siegen
学者数:
2.9K
论文数: 2.7K
被引数: 18
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