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 60µε were compensated.
This paper describes a new method for the classification and identification of two major types of defects in composites, namely delamination and matrix cracks, by classification of the spectral features of fibre Bragg grating (FBG) signals. In aeronautical applications of composites, after a damage is detected, it is very useful to know the type of damage prior to determining the treatment method of the area or perhaps replacing the part. This was achieved by embedding FBG sensors inside a glass-fibre composite, and analysing the output signal from the sensors. The glass-fibre coupons were subjected to mode-I loading under tension-compression and static tests, in order to induce matrix cracks and delamination damages respectively. Afterwards, using wavelet features extracted from spectral measurements of the FBG sensors, classification of the damage type was carried out by means of support vector machines as a general classification tool with a quadratic kernel.
In this paper we propose a novel approach to characterise barely visible transverse matrix cracks in composite structures using fibre Bragg grating optical sensors. Matrix cracks are one of the most prevalent types of damage in composite structures, and detecting them in the internal layers of composites has remained a challenge. In this paper, we will show that the formation of cracks in the internal layers of composite structures alters the side-lobes of the reflection spectra of FBG sensors by adding new harmonics to them. We argue that the spread and the location of these harmonics depends on both the mechanical properties of the composite material and the location of the crack along the length of the FBG sensor. Via computer simulations and experimental measurements we validate our hypotheses, and the results are in agreement with our model.
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