The detection of methane has always been an important part of coal mine safety. In order to improve the methane measurement accuracy in coal mines and to determine the influence of environmental interference factors on the measurement results, we designed a spherical, experimental chamber simulating the on-site environment of an underground coal mine containing methane, in which various environmental interference factors can be superimposed. The simulation chamber can generate a uniform and controllable dust environment, a controllable methane environment with concentrations below that which would trigger an alarm, controllable humidity, and environments characterized by other interference factors. Based on computational simulations of the experimental chamber with varying dust-particle-concentration distributions using a single particle size, an optimal design for the chamber has been realized in terms of the rapid mixing of dust and the flow field. Finally, we constructed an underground methane concentration measurement system for coal mines and assessed the influences of different dust concentrations and relative humidity values on the performance of methane measurements, providing a means for improving the measurement accuracy of underground coal mine, spectral, absorption-type methane sensors.

Vibration damping of jacket platforms is among the significant issues in marine science and engineering, and the design of active vibration control schemes is very important to ensure the stability and safety of the jacket platforms against external loadings. This paper provides three fuzzy output feedback $H_{\infty }$ controllers of the jacket platforms for irregular wave forces. By considering time-varying masses of jacket platforms, a Takagi-Sugeno (T-S) fuzzy dynamic model of the structure is established. Then fuzzy output feedback $H_{\infty }$ control schemes are developed via using output signals of the platform with current and/or are delayed. Several existence conditions of fuzzy output feedback $H_{\infty }$ controllers are derived. Simulation results demonstrate that the fuzzy output feedback $H_{\infty }$ control strategies are remarkable to suppress the vibration of structure. Moreover, by choosing proper delayed output information of the system, the presented delayed fuzzy output feedback $H_{\infty }$ control schemes outperform the conventional fuzzy output feedback $H_{\infty }$ control approach.

Deep displacement monitoring of rock and soil mass is the focus of current geological hazard research. In the previous works, we proposed a geophysical deep displacement characteristic information detection method by implanting magneto-electric sensing arrays in boreholes, and preliminarily designed the sensor prototype and algorithm of deep displacement three-dimensional (3D) measurement. On this basis, we optimized the structure of the sensing unit through 3D printing and other technologies, and improved the shape and material parameters of the permanent magnet after extensive experiments. Through in-depth analysis of the experimental data, based on the data query algorithm and the polynomial least square curve fitting theory, a new mathematical model for 3D measurement of deep displacement has been proposed. By virtue of it, the output values of mutual inductance voltage, Hall voltage and tilt measuring voltage measured by the sensing units can be converted into the variations of relative horizontal displacement, vertical displacement and axial tilt angle between any two adjacent sensing units in real time, and the measuring errors of horizontal and vertical displacement are tested to be 0–1.5 mm. The combination of structural optimization and measurement method upgrading extends the measurement range of the sensing unit from 0–30 mm to 0–50 mm. It shows that our revised deep displacement 3D measuring sensor can better meet the needs of high-precision monitoring at the initial stage of rock and soil deformation and large deformation monitoring at the rapid change and imminent-sliding stage.

To avoid strains in steel structures in special equipment caused by excessive alternating loads, which cause stress concentrations in local areas and reduce the strength and pressure‐bearing capacity of the steel structures, a method for evaluating the strain state of the steel structures using the eddy current effect is proposed, and the relevant testing device is developed. The RMS voltage and tension values were tested and fitted linearly with sampling time, and their linear correlations after fitting were 0.9978560 and 0.9967905, respectively. To investigate the method’s practical application, the effect of strain on the impedance of the eddy current probe was first studied theoretically, followed by the design and fabrication of a strain detection device comprised of an eddy current probe and a signal processing system. Finally, tensile strain experiments were carried out on 5 mm thick standard Q235 steel tensile specimens using a universal tensile machine and the linear equation of RMS voltage versus strain was obtained analytically. Theoretical and experimental tests have shown that the device can detect each strain stage and quantify the strain within the elastic stage by fitting a linear equation.

Underground three-dimensional displacement monitoring plays an important role in landslide disaster early warning. Compared with surface displacement and underground two-dimensional displacement monitoring, it can provide more accurate early warning information. Previously, the measurement device of underground three-dimensional displacement based on dual mutual inductance voltages was developed, which can collect millimeter-level displacement data. However, how to establish the relationship between mutual inductance voltages and underground three-dimensional displacement still faces huge challenges. In this work, the dual mutual inductance voltages at each inter-axis angle are treated as data maps. Then, they can serve as input to the convolutional neural network-based model (ConvRnet), who possess powerful image data mining capabilities. The experiment results show that the ConvRnet model significantly improves the prediction accuracy compared to the interpolation model and other machine learning models. The maximum errors of horizontal displacement and vertical displacement are 0.42mm and 0.32mm, respectively. The ConvRnet model combined with the data maps of dual mutual inductance voltages provides an effective tool for underground three-dimensional displacement measurement.