Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/79724
Title: Response covariance-based sensor placement for structural damage detection
Authors: Lin, JF 
Xu, YL 
Keywords: Sensor placement
Response covariance
Unit impulse response
Sensitivity analysis
Response independence
Damage detection
Issue Date: 2018
Publisher: Taylor & Francis
Source: Structure and infrastructure engineering, 2018, v. 14, no. 9, p. 1207-1220 How to cite?
Journal: Structure and infrastructure engineering 
Abstract: One important function of a structural health monitoring system is to detect structural damage in a structure. However, this is a very challenging task since the measurement is often incomplete in a civil structure due to a limited number of sensors. This paper presents a response covariance-based sensor placement method for structural damage detection with two objective functions for optimisation. The relationship between the covariance of acceleration responses and the covariance of unit impulse responses of a structure subjected to multiple white noise excitations is first derived. The response covariance-based damage detection method is then presented. Two objective functions based on the response covariance sensitivity and the response independence are, respectively, formulated and finally integrated into a single objective function for optimal sensor placement. Numerical studies are conducted to investigate the feasibility and effectiveness of the proposed method via a three-dimensional frame structure. Numerical results show that the proposed method with the backward sequential sensor placement algorithm is effective for damage detection.
URI: http://hdl.handle.net/10397/79724
ISSN: 1573-2479
EISSN: 1744-8980
DOI: 10.1080/15732479.2017.1402067
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

1
Citations as of Feb 15, 2019

WEB OF SCIENCETM
Citations

1
Citations as of Feb 16, 2019

Page view(s)

2
Citations as of Feb 18, 2019

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.