Automation and intelligent coal mining comprise the most important fields in coal mining technology research. The key to automation and intelligent coal mining is the automated mining of the working face, and accurate positioning of the shearer is one of the most important technologies in the automated mining process. However, significant defects in non-inertial navigation system (INS)-based methods have led to low positioning accuracy. In this paper, we propose a new shearer positioning technology to further improve the positioning accuracy of the shearer and monitor the shearer position in real time. The shearer positioning system proposed is based on the strapdown inertial navigation system (SINS). We conducted shearer positioning experiments with gyroscopes, accelerometers, and other inertial navigation instruments. The experimental results are thoroughly studied on the basis of error compensation techniques such as inertial instrument zero bias compensation and Kalman filter compensation. Compared with traditional shearer positioning technology, the experimental results show that the shearer positioning system based on SINS can achieve more accurate positioning of the shearer and can accurately reflect the running characteristics of the shearer working the mining face.

Most coal mines in China are currently mining close coal seams. Roadways in close coal seams, especially ultra-close coal seams, confronted difficulties in maintaining, including large deformation of the roadway, roof caving, rib spalling and floor heaving. This is mainly caused by the complicated stress and geological conditions, shattered roof, improper layout and support. To explore the issues mentioned above, the theoretical analysis was used to build a mechanical model and study the stress distribution under coal pillars, and FLAC3D modelling was adopted to build numerical models with different staggered distances. The optimal roadway layout was brought forward combining the result of numerical simulation and coal recovery rate. The field practice was carried out in the tailgate of panel 25301 to investigate the effect of the layout scheme. The results of field monitoring show that the roadway’s stability is well maintained in the mining process.

Support technology faces challenges in view of the large deformation of surrounding rock in three-soft coal roadways under high horizontal stress in Zijin Coal Mine, China. Geostress near the tested working face of the mine was measured and its distribution law was analyzed through theoretical analysis, numerical simulation analysis, and field measurement. The original supporting scheme of the three-soft coal roadway on the tested working face was analyzed to discover the deformation and failure mechanism of the surrounding rock of the original supporting roadway and the control measures. An optimized support scheme of H-G (hollow grouting) anchor cables, high strength bolts, W-shaped steel belts, metal meshes, and sprayed concretes was proposed for field applications. Based on the roadway in the tested 3201 working face at Zijin Coal Mine, the technical parameters for optimizing the combined support of the roadway were determined. The following results were be obtained through field measurement. The roadway was kept intact after excavation and the optimized support scheme was adopted in the three-soft coal roadway. No obvious deformation in appearance existed in the roof, floor, and roadway coal sides. Compared with the original support scheme, the stability of the roadways was improved visibly. The displacement of the roadway roof decreased from 100 to 30 mm, and that of the roadway coal walls decreased from more than 100 mm to less than 50 mm. This work verifies the effectiveness of a combined support scheme of H-G anchor cables, high strength bolts, W-shaped steel belts, metal meshes, and sprayed concretes to control deformations of surrounding rock in three-soft coal roadways. The new support scheme has good social and economic benefits and can be used as a reference for other roadway supports under similar conditions.

In order to solve the key technical problem of accurate perception of the straightness of a scraper conveyor in intelligent mining of coal mines, a three-dimensional (3D) curvature sensor of fiber Bragg grating (FBG) is designed and developed based on the curvature sensing principle of FBG. The grating string embedded in the FBG curvature sensor is simulated under the radius of curvature of 100 m, 200 m, 300 m, 400 m and 500 m. The results show that the strain of the built-in FBG string is linearly related to the curvature at the bend of the FBG string. The simulation results are consistent with the theoretical results, which verifies the rationality of the sensor design. Based on the discrete curvature information obtained by the 3D curvature sensor of FBG, the curve reconstruction algorithm is derived by using the method of fitting recursion, and the bending shape fitting perception and curve reconstruction of the scraper conveyor is realized. A test platform for the three-dimensional bending of the scraper conveyor is set up. The measuring effect of perceived and actual shape at the straight and bending states of the scraper conveyor is verified. The test results show that the measured curve is basically the same as the perceptual curve in the three-dimensional bending configuration of the scraper conveyor, and the error of the direction of each coordinate axis is not more than ±15 mm, which can meet the requirements of the accuracy of detecting the straightness of the intelligent work.

The hydraulic support is the key equipment of surrounding rock support in a stope, and thus monitoring the attitude of the hydraulic support has an important guiding role in the support selection, operation control and rock pressure analysis of the working face. At present, attitude monitoring technology for hydraulic support mainly includes inertial measurement, contact measurement and visual measurement. Aiming at the technical defects of imperfect attitude perception models, incomplete perception parameters and the low decision-making ability of such systems, the fiber Bragg grating (FBG) pressure sensor and the FBG tilt sensor are developed independently by combining with FBG sensing theory. The pressure sensitivity of the FBG pressure sensor is 35.6 pm/MPa, and the angular sensitivity of the FBG tilt sensor is 31.3 pm/(°). Additionally, an information platform for FBG sensing monitoring for hydraulic support attitude is constructed based on. NET technology and C/S architecture. The information platform realizes real-time monitoring, data management, report management, production information management and data querying of hydraulic support attitude monitoring data. An AdaBoost neural network hydraulic support working resistance prediction model is established using MATLAB. The AdaBoost neural network algorithm successfully predicts the periodic pressure of the coal mining face by training with the sample data of the working resistance of the hydraulic support. The predicting accuracy is more than 95%.

Intelligent mining is the demand and inevitable development direction of China’s coal industry, and the shearer attitude based on the 3D spatial scale is the essential basic information for realizing intelligent mining at the fully mechanized mining face. The traditional shearer attitude monitoring technology cannot meet the accuracy requirements of attitude sensing of mining equipment at an intelligent working face. In this paper, based on the basic principle of strapdown inertial navigation system (SINS), a real-time solution algorithm for shearer attitude is constructed, and the specific process of SINS coarse alignment and fine alignment is studied. The main error terms affecting the accuracy of SINS are analyzed, and the initial alignment error, installation deviation angle error, and vibration effect error are compensated and analyzed through simulation one by one. According to the working characteristics of the shearer, the coning error and sculling error of the shearer’s SINS are compensated and analyzed through simulation. This paper provides a complete set of theories and methods for improvement of the accuracy of the shearer attitude sensing in underground operation; simulation results also verify that the theories and methods can significantly improve the perception accuracy which can provide theoretical and technical reference for the production state prediction of fully mechanized mining face and intelligent control of underground mining equipment.