Calculation of the Mean Strain of Smooth Non-uniform Strain Fields Using Conventional FBG Sensors

Journal Article (2018)
Author(s)

Aydin Rajabzadeh (TU Delft - Signal Processing Systems)

R Heusdens (TU Delft - Signal Processing Systems)

Richard C. Hendriks (TU Delft - Signal Processing Systems)

R.M. Groves (TU Delft - Structural Integrity & Composites)

Research Group
Signal Processing Systems
Copyright
© 2018 Aydin Rajabzadeh, R. Heusdens, R.C. Hendriks, R.M. Groves
DOI related publication
https://doi.org/10.1109/JLT.2018.2849212
More Info
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Publication Year
2018
Language
English
Copyright
© 2018 Aydin Rajabzadeh, R. Heusdens, R.C. Hendriks, R.M. Groves
Research Group
Signal Processing Systems
Bibliographical Note
Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. @en
Issue number
17
Volume number
36
Pages (from-to)
3716-3725
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Abstract

In the past few decades, fibre Bragg grating (FBG) sensors have gained a lot of attention in the field of distributed point strain measurement. One of the most interesting properties of these sensors is the presumed linear relationship between the strain and the peak wavelength shift of the FBG reflected spectra. However, subjecting sensors to a non-uniform stress field will in general result in a strain estimation error when using this linear relationship. In this paper we propose a new strain estimation algorithm that accurately estimates the mean strain value in the case of smooth non-uniform strain distributions. To do so, we first introduce an approximation of the classical transfer matrix model, which we will refer to as the approximated transfer matrix model (ATMM). This model facilitates the analysis of FBG reflected spectra under arbitrary strain distributions, particularly by providing a closed-form approximation of the side-lobes of the reflected spectra. Based on this new formulation, we derive a maximum likelihood estimator of the mean strain value. The algorithm is validated using both computer simulations and experimental FBG measurements. Compared to state-of-the-art methods, which typically introduce errors of tens of microstrains, the proposed method is able to compensate for this error. In the typical examples that were analysed in this study, mean strain errors of around 60° were compensated.

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