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• Three optic methods monitoring were applied in the model test. • A new method to forecasting goaf pressure was proposed. • The stress evolution model of coal seam mining space is established. • This research is a combination of mining engineering and fiber optic sensing, provide the theoretical basis for fiber optic sensing technology to be better applied in mining engineering. The spatial distribution of rock mass stress in goaf seriously affects the stable operation of underground reservoir. Measurement of stress distribution in the goaf of coal mines is difficult due to the complex internal environment and other restrictive conditions of goaf. This paper presents a new method of monitoring the deformation of key stratum in overburden by optical fiber sensing, and then characterizing the pressure of goaf. The internal mechanism of key stratum breakage and the variation of goaf stress is investigated by physical model with the geometric dimension of 3 (length) × 1.3 (height) × 0.4 (width) m), in which the Brillouin Optical Time Domain Analysis (BOTDA) fiber-optic sensing technology and Fiber Bragg Grating (FBG) technology are used to monitor the stability of key stratum and the non-contact Digital Image Correlation (DIC) technique is employed to provide real-time deformation measurements. Combining the fracture mechanics model of key strata with the parabolic function model of subsidence trajectory, the stress evolution model of coal seam mining space is established for the first time, the goaf pressure change and the corresponding relation between the key layer breakage was quantitatively characterized. The research results show that: The fracture location of the key strata is consistent with the peak stress location of the goaf. The breaking limit strain threshold value of the key strata based on optical fiber sensing detection is 4000 με. Through the inversion of optical fiber monitoring results, the fracture degree and range of overburden rock in goaf are obtained, thus reflecting the position and change of the peak pressure in goaf. The consistency between key layer deformation and goaf pressure variation is verified. [ABSTRACT FROM AUTHOR]

• A self-compensation mechanism of temperature was introduced in the sensor. • The model developed in this paper has a high analyzing speed without losing accuracy and therefore can be a powerful tool for design and optimizing. • A theoretical model for analysis of a moisture sensor in the transformer oil with a complex refractive index (RI) profile has been developed. This study presents a theoretical model that enables a fast and precise calculation of a sampled apodized fiber Bragg grating and the related sensor. Transfer matrix method was used since there is no analytical solution due to the complex refractive index profile. Errors brought by the uniform refractive index profile approximation were compensated, which allows a smaller number of divided segments and therefore a fast and memory efficient simulation. This compensation method realizes a high speed and high precision simulation. The theoretical spectra of both the grating and the sensor agrees well with those given by the experiments. According to the experiment, the sensitivity of the moisture sensor is −0.6165 pm/ppm in mineral oil. This theoretical model provides a new choice for the researchers and developers to design and optimize the sensing structure instead of a blind try and error process. [ABSTRACT FROM AUTHOR]

• A packaging method that can accurately, continuously and adjustably apply pre-stress to FBGs. • An instrument capable of realizing the pre-stressed packaging method. • Sensing performance of the FBG sensor is improved with the pre-stressed packaging method. Packaging technology is a key factor in fiber Bragg grating (FBG) sensing, which can greatly affect the sensing performance of FBG sensors. In this paper, a pre-stressed packaging method for FBG sensors is proposed. The method enables the pre-stress applied on the FBG to be continuously and accurately adjusted when the FBG sensor is packaged, thereby ensuring the stability of the pre-stress. In order to implement the method, an instrument is designed and fabricated as well. The proposed method is demonstrated through some experiments, experimental researches indicate that using the proposed method, the spectrums of the fourteen packaged FBG sensors have no chirping nor broadening the spectral 3 dB bandwidth. The average temperature-dependent sensitivity coefficient of fourteen sensors is 2.03 × 10−5/℃, the relative error is 1.7%, and the average linearity is better than 0.999. So, the proposed pre-stressed packaging method has advantages of great stability, good repeatability, easy operation and low cost. [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]

• The modified artificial fish swarm algorithm is proposed to optimize the network. • Elliptical strain sensing model of fiber Bragg grating sensor is established. • The exponential attenuation model between sensor and the signal source is constructed. • Attenuation factors of the three adaptive step size are introduced into the algorithm. • The performance of the modified algorithm is better than PSO and GA. The effectiveness of sensor networks depends largely on the coverage provided by sensor deployment schemes. In order to improve the coverage rate of sensor network, it is necessary to optimize the sensor layout. In this paper, an optimized layout method of fiber Bragg grating (FBG) sensor network based on modified artificial fish swarm algorithm (MAFSA) is proposed. Firstly, the elliptical sensing model of FBG is established. According to the transfer characteristics of FBG, the exponential attenuation model between the sensor node and signal source is constructed. Secondly, taking the network coverage rate as the objective function, the movement of nodes is analogized to the behavior of artificial fish such as swarming, following and preying. In the process of status updating for artificial fish, aiming at the defects of the standard artificial fish swarm algorithm (AFSA) in optimization, three adaptive step methods are proposed. The convergence accuracy and speed of the algorithm are improved. Finally, the performance of the MAFSA is evaluated with particle swarm optimization algorithm and genetic algorithm. The results indicate that the MASFA has stronger search performance and can make the sensor node layout more reasonable, which is suitable for solving the problem of the sensor layout optimization. [ABSTRACT FROM AUTHOR]