Hydraulic support is an indispensable equipment for coal mining face. To detect the position and posture of the hydraulic support and automatically control its straightness at the mining working face, we developed a device based on the principle that three noncolinear points determine a plane and proposed a novel method using it. First, the sensor parameters in the device were experimentally calibrated. Second, the accuracy of the device was evaluated in the single point detection experiment by measuring the position of the nine datum points. Through actual testing, our detection accuracy can reach 24.82mm. Third, our method was validated in the experiment of the plane detection. Finally, the detection device was installed on the hydraulic support mode. The change of the position and posture of the canopy in the movement of the hydraulic support lifting leg, realized the detection of position and posture of the adjacent hydraulic support, and the feasibility of the detection method was verified. Our research on the position and posture detection of the hydraulic support can lay an important theoretical foundation for the automatic control of the straightness of mining working face and the surveillance of the dangerous posture of the hydraulic support. Furthermore, it can provide a novel monitoring approach for preventing collapse of hydraulic supports under complex geological conditions such as large dip angles and large mining heights.

The position and posture of the hydraulic support seriously affect the efficiency of coal mining and the safety of coal production. However, most of the current detection technologies have poor reliability, and the detection methods are difficult to adapt to the complex environment of coal mines. To effectively monitor the hydraulic support, and thereby improve the efficiency of coal mining, we propose a new method to detect the relative position and posture of the hydraulic support. The method is based on the mathematical idea that three points can determine a plane. Firstly, we use angle sensors and displacement sensors to build the detection device, use STM32 microprocessor to collect data, and realize real-time display of the data on the PC. Then, we conduct a single point detection experiment and a plane moving experiment, and combined the particle swarm optimization (PSO) algorithm to optimize the data. Experiment results show that the relative error of the single-point detection can reach 0.53%. Thirdly, we carry out a detection experiment of three points on the plane. Experiment results show that the detection accuracy of the two planes can reach 0.2°. Finally, to test the monitoring effect of the detection device on the hydraulic support, we carry out the relative position and posture detection experiment of the canopy. The experiment results show that the device can effectively detect the posture change of the canopy when the hydraulic support are moving. The method we use is contact measurement, which has high reliability and strong stability. The research on the relative position and posture detection of hydraulic support provides a reliable method to monitor the support working status. It lays the foundation for the intelligent control of the mining working face straightness and the perception of the support posture.

The scraper conveyor is the unique transport equipment in coal mining face. Its chain drive system is the core subsystem. The dynamic characteristics of the chain drive system need monitoring for a safe mining. Existing monitoring methods include controlling the chain tension, chain speed and motor current. However, the above-mentioned methods have many disadvantages, such as difficult and costly to implement in mining face, due to the poor working environment and the limitations of the scraper conveyor’s structure. This study proposes a monitoring method for the dynamic characteristics of the scraper conveyor on the basis of the speed difference between the head and the tail sprockets. Such an initiative is carried out to study the dynamic characteristics of the scraper conveyor under real working conditions and describe its running state. The speed difference of the head and tail sprockets under different chain speeds, terrains, and loads is monitored and studied. Research results show that the average and maximum speed differences are 0.797% and 0.990%, respectively. Due to the complex terrain, the tighter the scraper chain gets, the smaller the difference of the rotational speed is, compared with the flat working condition. The speed difference under load greatly increases compared with no load. Research results provide data support for the research of the chain drive system dynamics of the scraper conveyor. These results will also provide a new idea for the monitoring of the operating state of the scraper conveyor.

In the study of coal mining, the position and posture, the working status, and the straightness of the hydraulic support are critical to the safety of the coal mine. To detect and control the position of a hydraulic support, a model for solving the self-position and posture and a model for solving the relative position and pose of a hydraulic support based on the principle of three points determining a plane in space were established. Then, with the help of particle swarm optimization algorithm and Bang-Bang control algorithm, the hydraulic support position and the posture detection and control system are constructed. Finally, according to the requirements of sensor data collection and online monitoring of the position and posture of hydraulic support, a hydraulic support posture detection and control system is developed, and the simulation experiments of hydraulic support cylinder movement sample collecting, hydraulic support following motion and hydraulic support independent following machine are carried out on the self-developed three-machine intelligent control simulation experiment platform of the working face. However, the test under this simulation platform did not add the load of the top beam. The results show that the self-pose solution model and the relative-pose solution model of the hydraulic support can be used to quickly monitor the position and posture of the hydraulic support group. In the hydraulic support cylinder following motion movement, the largest following error of each cylinder is the balance cylinder, and its maximum error is 2.6 mm. In the hydraulic support independent following machine simulation, the maximum motion error of the support base is 2.2 mm. The position and posture detection and control system developed for hydraulic support can realize accurate detecting and control the position and posture of hydraulic support, as well as meet the requirements for industrial applications.

A scraper conveyor is important in coal mining. During operation, its working performance is affected by chain speed fluctuations, terrain fluctuations, and load changes. Thus, evaluating the influence of these factors on the dynamic properties of a scraper conveyor is important. This study first built a dynamic property test bench. Then, the vibration signals of the reducer output shaft were measured under various chain speed, terrain, and load conditions. Finally, the dynamic properties of a scraper conveyor were evaluated by conducting a frequency domain analysis of the measured vibration signals. The results show that the output shaft of the motor, the second shaft, and the second-stage meshing gear of the reducer are sensitive to external factors. Under the terrain conditions of “horizontal + vertical” bending, the middle chute was the most sensitive to the meshing frequency of the sprocket chain. This type of condition had a significant influence on the scraping phenomenon of the scraper and the middle chute. Under various load conditions, the amplitude of each shaft of the reducer decreased, especially the amplitude of the motor output shaft, but the scraping amplitude of the scraper and middle chute greatly increased. This study is of great significance for improving the dynamic properties and structural optimization of scraper conveyors.

Hydraulic supports are important equipment used in coal mining. Detecting the real-time attitude and straightness of hydraulic support group in unmanned working faces of coal mines in complex geological conditions is difficult. To address this issue, a method that measures the position and attitude of the hydraulic support using binocular vision detection-aided mark points was proposed. First, a combination of binocular vision positioning and a convolutional neural network (CNN) object detection is used to achieve the three-dimensional (3D) location of mark points. Then, the attitude calculation model of the hydraulic support based on the dual quaternion method is established. This method achieves the space attitude detection between multiple hydraulic supports and the straightness detection of hydraulic support group in 3D space. In the laboratory environment, the position and attitude detection test of the hydraulic support is obtained through the working face equipment position detection test bench. This method can adapt to the worse working environment in coal mine production and can provide early data for the automatic control of the straightness of the working face.