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• Multiparameter sensing system using a single FBG with a polarization-assisted analysis. • Strain, torsion and temperature for the analysis of polarization and spectral components. • The relative errors are below 0.5% for each mechanical load condition. • A root mean squared error of 2.91 °C is obtained for the temperature analysis. This paper presents the development of a multiparameter sensing system using a single fiber Bragg grating (FBG) with a polarization-assisted analysis. In this case, the FBG sensor is subjected to axial strain, torsion angle and temperature for the analysis of polarization components and spectral features variations. In addition, the responses at two orthogonal polarization states are compared to evaluate the feasibility of developing a polarization-based analysis for multifeature regression. Results show the differences in the sensors responses considering the full spectrum and the polarization components, where non uniform strain components lead to differences in the polarization states, which are used on the simultaneous analysis of different mechanical loadings combinations. Furthermore, the simultaneous estimation of axial strain, torsion angle and temperature resulted in small relative errors when the whole range of the tests are considered. The relative errors are below 0.5 % for each mechanical load condition, whereas a root mean squared error of 2.91 °C is obtained for the temperature analysis. Thus, the proposed approach resulted in a suitable alternative for three parameters sensing using only one FBG in standard single mode silica optical fiber without any structural modification, which can lead to a new approach on spectrally efficient sensor systems for practical applications. [ABSTRACT FROM AUTHOR]

• Machine learning approach for automatic estimation of responses using different TFBGs. • Estimation based on the optical spectral characteristics of TFBGs as a function of RI. • Root mean squared errors as small as 0.008 RIU for the trained samples. • The approach can be used to enhance the reproducibility of large set of TFBG sensors. This paper presents the development of an approach for automatic estimation of the regression coefficients as a function of the refractive index (RI) in Tilted Fiber Bragg Gratings (TFBGs) based on their optical spectral characteristics. The methodology is based on machine learning approach, where different samples are analyzed in various test conditions to obtain a dataset for the training of a kNN regression algorithm. In this case, the spectral characteristics related to the wavelength spacing between adjacent cladding mode resonances, number of valleys and amplitude of the normalized spectra are correlated with the linear regression algorithm for RI estimation using the machine learning approach. The analysis are performed as a function of the wavelength shift and optical power, considering two regions on the transmitted spectrum and the envelope of the curve. Furthermore, the number of valleys and the area of the envelope curve are also analyzed as a function of the refractive index, which result in six analyzed features. The Principal Components Analysis (PCA) is used to reduce the number of features to two principal components. The results of the proposed algorithm are obtained with different trained and untrained samples, which indicates a root mean squared error (RMSE) of around 0.003 RIU for the trained samples and 0.008 RIU for the untrained ones. Therefore, the results indicate the usefulness of the proposed approach, which can be used to enhance the reproducibility of large set of TFBG sensors in practical applications. [ABSTRACT FROM AUTHOR]

This paper presents the development, signal analysis and application of an FBG array embedded smart rubber carpet for 2D shape reconstruction. The proposed sensor system is based on the optical fiber integration between two rubber layers, where the optical fibers have 7 FBGs for the quasi-distributed force assessment. The sensor system analysis is performed at room temperature in difference set of tests, namely the stability tests (without any applied force), the force characterization tests (forces applied directly on the FBG region) and the shape reconstruction tests (different forces applied at different positions in the carpet). The stability tests indicated a maximum variation below 0.02 nm considering all 7 FBGs, whereas the force characterization tests indicated a mean sensitivity of around 80 N/nm. However, it is worth to mention that there is a variation on the sensitivities considering all FBGs, which is related to minor thickness differences in the rubber layer that directly effects the strain transmission from the rubber layer to the optical fiber beneath it. Thereafter, the shape reconstruction occurs in two steps, the first one is the cubic interpolation between the FBGs, considering the boundary conditions on the carpet following the beam theory. Then, the k-nearest neighbors regression algorithm is used on the force distribution assessment with a dataset divided into training and testing with a 70/30 proportion. The shape reconstruction results indicate the feasibility of the proposed approach, where root mean squared errors below 0.65 N are obtained in the range of applied force between 3 N and 35 N. Therefore, the proposed smart carpet as a feasible option for different 2D shape reconstruction applications ranging from biomechanics to structural health monitoring. • Force sensor array using Fiber Bragg Gratings embedded in rubber carpet. • 2D Shape reconstruction using machine learning algorithms. • Relative errors on the shape reconstruction below 3%. [ABSTRACT FROM AUTHOR]

• SPR-based optical fiber sensor for salinity measurement. • FBG sensors for salinity assessment through thermal responses. • Combination of SPR and FBG sensors for higher accuracy in salinity estimation. This paper presents a novel optical fiber sensor system for salinity monitoring using data fusion in different optical fiber sensors approaches. In this case, a surface Plasmon resonance (SPR) sensor in a D-shaped optical fiber is developed in conjunction with a fiber Bragg grating (FBG). The SPR sensors are able to detect the liquid sample salinities through the refractive index variation of the different samples. In contrast, the FBG sensors estimate the salinity through the thermal dynamics of the samples. Results show a sensitivity of 164.0 nm/% and 12.5 pm/°C for the SPR and FBG sensors, respectively. Thus, the combination between the SPR and FBG sensors result in an improvement of the salinity monitoring of different samples, where a root mean squared error of 0.001% is obtained. [ABSTRACT FROM AUTHOR]

• Optical fiber sensors for impact amplitude and localization. • Analysis of spectral features of FBG embedded in diaphragm. • Impact localization estimation in the x and y planes with errors below 5%. • Diaphragm-embedded FBG sensor for force assessment with errors below 0.47 N. This paper presents the development, numerical and experimental analyses of a fiber Bragg grating (FBG) based sensor for force amplitude and localization (in 2D plane) detection. The sensor system is based on a diaphragm-embedded FBG, where the spectral features, namely wavelength shift, optical power and full width at half maximum (FWHM) are analyzed as a function of the force application parameters (amplitude and position). The diaphragm results in a higher spatial range for the sensor as its larger dimensions (when compared with the optical fiber) enable the strain transmission to the optical fiber even when the force is not directly applied to the fiber. In order to verify the proposed approach, finite element method is applied on the strain analysis in the optical fiber when the diaphragm is subjected to forces at different positions. Then, the FBG reflected spectra are simulated considering the strain distribution obtained at each condition, where there are variations not only in the Bragg wavelength, but also in the FWHM and optical power as a function of the force amplitude and position. The experimental analysis confirms the results numerically obtained and shows the possibility of simultaneously measuring force amplitude and position (x and y components) using a single FBG. Results show the feasibility of the method, where root mean squared errors of 0.47 N (in 10 N range), 0.48 mm (in 10 mm range) and 0.33 mm (in 6 mm range) are found for the force, horizontal (x-) and vertical (y-) components estimations, respectively. Thus, the proposed approach shows relative errors below 5% and is an interesting approach for sub-millimeter resolution force and impact detection, which can also be used in multiplexed systems with the advantage of higher spectral efficiency, since a single FBG can provide multiple measurements that are commonly made employing a higher number of sensors. [ABSTRACT FROM AUTHOR]

• Estimation of the level using FBG temperature sensors array. • Machine learning to identify at which fluid each FBG is submerged with 89.54% accuracy. • Proposed Mixed Model (MM) uses combination of regression algorithms to level estimation. • MM has a RMSE of 3.58 cm, resulting 57.93% improvement compared to other algorithms. This paper proposes the use of the fiber Bragg grating (FBG) temperature sensors array to estimate the fluid level. The tank is 100 cm in height and 30 cm in width, with 9 FBG sensors distributed along with the tank height. This work proposes level estimation in two steps: level detection (classification) and level estimation (regression). The level detection consists of finding under which FBG the level is. We dichotomize the classes in water and not water: air. For the detection, we use the following Machine Learning (ML) algorithms: Logistic Regression (LogR), Decision Tree (DT), and Support Vector Machine (SVM). We chose the algorithms based on their usability in literature and theoretical consolidation. The algorithm with the best results among the ones tested is DT, resulting in an average accuracy of 89.54%. For the level estimation, we use the wavelength shift in conjunction with the classification obtained via DT. The level estimation consists of estimating in cm the location of the level. The algorithms chosen are Weighted Linear Regression (WLR), Support Vector Regression (SVR), SVR with kernel selection minimize cost (SVRmin). Each of the applied algorithms presented better results in different level intervals. Thus, we propose the Mixed Model (MM), which selects the lowest Root Mean Square Error (RMSE) among the tested regression algorithms at each level and associates it with it. The MM has a RMSE of 3.56 cm, which is approximately four times smaller than when using WLR. The SVM and SVMmin have RMSE of 6.28 cm and 6.14 cm, respectively. [ABSTRACT FROM AUTHOR]