Ming Yan

Shearer positioning technology can effectively improve the automation level of fully mechanized mining face. The Strapdown Inertial Navigation System (SINS) and odometer integrated navigation system assisted by nonholonomic constraint (NHC) and closed path optimal estimation model (CPOEM) has been widely studied in shearer positioning. Due to the difficulty in accurately measuring and controlling the distance and direction of the hydraulic support, abnormal errors often occur between the predicted position calculated by CPOEM and the actual position of shearer, and measurement noise cannot be accurately determined, thereby seriously reducing the positioning accuracy of shearer. To address this issue, an adaptive and robust shearer navigation system based on the interacting multiple model (IMM) algorithm is proposed in this paper. Firstly, maximum correntropy criterion Kalman filter (MCCKF) is used to replace KF to reject outliers in predicted positions, and adaptive kernel bandwidth is introduced to MCCKF to further enhance robustness; Secondly, Sage-Husa Adaptive Kalman Filter (SHAKF) is used to estimate the measurement noise in the predicted position in real-time; Finally, MCCKF and SHAKF are used as two sub filters of IMM working in parallel, and the state estimation of each sub filter is fused based on the quality of innovation. Through simulation and experiment, it has been proven that the navigation scheme proposed in this paper can integrate the advantages of MCCKF and SHAKF, avoiding the interference of outliers and estimating measurement noise in real-time, effectively improving the positioning accuracy of shearer.

The integrated navigation system of strapdown inertial navigation system (SINS) and odometer (OD) is a commonly used vehicle positioning system. The installation errors caused by the difference between the installation positions of SINS and OD mainly include the installation deflection angle and lever arm, which will reduce the positioning accuracy. The traditional calibration method is to expand the installation deflection angle error and lever arm error into state variables, the velocity difference under nonholonomic constraint (NHC) is taken as the measurement to establish the Kalman filter model for online calibration, but this method seriously depends on the sensor accuracy. In order to calibrate the installation errors of low-precision SINS, this paper improves NHC, adds the difference between the centripetal acceleration measured by odometer and calculated by SINS as measurement on the basis of the original measurement, and introduces a low-pass filter to reduce the interference of vehicle vibration on the accelerometer.The experimental results show that the proposed algorithm has better convergence of the installation deflection angle, and the error between the estimation results of the forward lever arm and the actual measurement results is less than 0.1m; After compensating the installation errors of the improved NHC, the maximum positioning errors in the east and north directions are reduced by 63.19% and 43.51% respectively, the height error is also greatly reduced, and the positioning accuracy of the integrated navigation system is significantly improved.

The key technology to realize intelligent unmanned coal mining is the strapdown inertial navigation system (SINS); however, the gradual increase of cumulative error during the working process needs to be solved. On the basis of an SINS/odometer (OD)-integrated navigation system, this paper adds magnetometer (MAG)-aided positioning and proposes an SINS/OD/MAG-integrated shearer navigation system. The velocity observation equation is obtained from the speed constraints during shearer movement, and the yaw angle observation equation is obtained from the magnetometer output. The position information of the SINS output is calibrated using these two observations. In order to improve the fault tolerance of the integrated navigation system, an adaptive federated Kalman filter is established to complete the data fusion of the SINS. Experimental results show that the positioning accuracy of the SINS/OD/MAG-integrated navigation system is 75.64% and 74.01% higher in the east and north directions, respectively, than the SINS/OD-integrated navigation system.

The technology of coal-rock interface recognition is the core of realizing the automatic heightening technology of shearer’s rocker. Only by accurately and quickly identifying the interface of coal and rock can we realize the fully automatic control of shearer. As the only one used in the actual detection of coal mining machine drum cutting coal seam after the thickness of the remaining coal seam detection method, natural γ-ray has a very practical advantage. Based on the relationship between the attenuation of the natural γ-ray passing through the coal seam and the thickness of the coal seam, the mathematical model of the attenuation of the natural γ-ray penetrating coal seam is established. By comparing the attenuation intensity of γ-ray with or without brackets, it is verified that the hydraulic girders will absorb some natural γ-rays. Finally, this paper uses the ground simulation experiment and the field experiment to verify the correctness of the mathematical model and finally develop the natural γ-ray seam thickness sensor. The sensor has the function of indicating the thickness of the coal seam, measuring the natural γ-ray intensity, and storing and processing the data.