Structural health monitoring of GFRP sandwich beam structures
Structural health monitoring of GFRP sandwich beam structures
The use of GFRP sandwich materials is increasingly becoming popular. However, during the manufacturing process internal and external defects could occur. A vibration based NDE approach, using an SHM system, offers the possibility of examining the GFRP sandwich structure while it is in service. The research presented in this thesis highlights the various aspects of implementing this SHM based approach by studying and making improvements to individual components of the SHM system, namely an embedded sensor network and intelligent signal processing algorithms. A beam configuration was chosen as the test platform of choice to implement the SHM system for the GFRP sandwich structure.
Considering the issue of embedding a sensor network within the GFRP sandwich structure, a modified vacuum infusion manufacturing technique was conceived which enabled a small network of optical FBG strain sensors to be successfully embedded between the skin and core of the GFRP sandwich beam structure.
The intelligent signal processing algorithm developed utilised the wavelet transform estimated Lipschitz exponent as a damage sensitive signal feature. It was first successfully demonstrated, in its spatially varying form, for characterising debonds in numerically simulated models of GFRP sandwich beams. Subsequent experimental investigation of manufactured GFRP sandwich beam specimens, containing embedded FBG strain sensors and subjected to random loading conditions, showed that key features in the singularity spectrum, which is the spread of Lipschitz exponents, and its associated moment generating function, not only allowed successful identification of a variety of defects, but they also enabled quantitative identification of their approximation position on the GFRP sandwich beam specimen.
University of Southampton
Dawood, Tariq Ali
3f70cd2f-a7fc-4e92-b6c7-f381118f5e6b
2006
Dawood, Tariq Ali
3f70cd2f-a7fc-4e92-b6c7-f381118f5e6b
Dawood, Tariq Ali
(2006)
Structural health monitoring of GFRP sandwich beam structures.
University of Southampton, Doctoral Thesis.
Record type:
Thesis
(Doctoral)
Abstract
The use of GFRP sandwich materials is increasingly becoming popular. However, during the manufacturing process internal and external defects could occur. A vibration based NDE approach, using an SHM system, offers the possibility of examining the GFRP sandwich structure while it is in service. The research presented in this thesis highlights the various aspects of implementing this SHM based approach by studying and making improvements to individual components of the SHM system, namely an embedded sensor network and intelligent signal processing algorithms. A beam configuration was chosen as the test platform of choice to implement the SHM system for the GFRP sandwich structure.
Considering the issue of embedding a sensor network within the GFRP sandwich structure, a modified vacuum infusion manufacturing technique was conceived which enabled a small network of optical FBG strain sensors to be successfully embedded between the skin and core of the GFRP sandwich beam structure.
The intelligent signal processing algorithm developed utilised the wavelet transform estimated Lipschitz exponent as a damage sensitive signal feature. It was first successfully demonstrated, in its spatially varying form, for characterising debonds in numerically simulated models of GFRP sandwich beams. Subsequent experimental investigation of manufactured GFRP sandwich beam specimens, containing embedded FBG strain sensors and subjected to random loading conditions, showed that key features in the singularity spectrum, which is the spread of Lipschitz exponents, and its associated moment generating function, not only allowed successful identification of a variety of defects, but they also enabled quantitative identification of their approximation position on the GFRP sandwich beam specimen.
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Published date: 2006
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Local EPrints ID: 466194
URI: http://eprints.soton.ac.uk/id/eprint/466194
PURE UUID: 2eb19b40-5303-4e96-81c0-a5758da978f1
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Date deposited: 05 Jul 2022 04:43
Last modified: 16 Mar 2024 20:33
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Author:
Tariq Ali Dawood
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