This study proposes a virtual monitoring method for hydraulic supports based on digital twin theory. This method includes an information model, which monitors the attitudes of the hydraulic supports using an information fusion algorithm. This is combined with a virtual digital model that simulates the actual hydraulic support. Important technological aspects of the monitoring system were achieved at the real-virtual interface, which digitizes and modularizes the management and connection processes of the hydraulic supports over its entire life cycle. Finally, experiments were conducted and the real-time virtual images were synchronized with the actual motion of the support within one second, and the error range between the virtual and actual support postures did not exceed 1%. This study demonstrates the potential of this novel combined approach to make it possible to effectively monitor and make decisions regarding the safe and efficient operation of mechanized mine equipment that employ hydraulic supports.

To reduce the difficulty of acquiring and transmitting data in mining hoist fault diagnosis systems and to mitigate the low efficiency and unreasonable reasoning process problems, a fault diagnosis method for mine hoisting equipment based on the Internet of Things (IoT) is proposed in this study. The IoT requires three basic architectural layers: a perception layer, network layer, and application layer. In the perception layer, we designed a collaborative acquisition system based on the ZigBee short distance wireless communication technology for key components of the mine hoisting equipment. Real-time data acquisition was achieved, and a network layer was created by using long-distance wireless General Packet Radio Service (GPRS) transmission. The transmission and reception platforms for remote data transmission were able to transmit data in real time. A fault diagnosis reasoning method is proposed based on the improved Dezert-Smarandache Theory (DSmT) evidence theory, and fault diagnosis reasoning is performed. Based on interactive technology, a humanized and visualized fault diagnosis platform is created in the application layer. The method is then verified. A fault diagnosis test of the mine hoisting mechanism shows that the proposed diagnosis method obtains complete diagnostic data, and the diagnosis results have high accuracy and reliability.

In a fully mechanized coal‐mining face, the positioning and attitude of the shearer and scraper conveyor are inaccurate. To overcome this problem, a joint positioning and attitude solving method that considers the effect of an uneven floor is proposed. In addition, the real‐time connection and coupling relationship between the two devices is analyzed. Two types of sensors, namely, the tilt sensor and strapdown inertial navigation system (SINS), are used to measure the shearer body pitch angle and the scraper conveyor shape, respectively. To improve the accuracy, two pieces of information are fused using the adaptive information fusion algorithm. It is observed that, using a marking strategy, the shearer body pitch angle can be reversely mapped to the real‐time shape of the scraper conveyor. Then, a virtual‐reality (VR) software that can visually simulate this entire operation process under different conditions is developed. Finally, experiments are conducted on a prototype experimental platform. The positioning error is found to be less than 0.38 times the middle trough length; moreover, no accumulated error is detected. This method can monitor the operation of the shearer and scraper conveyor in a highly dynamic and precise manner and provide strong technical support for safe and efficient operation of a fully mechanized coal‐mining face.

The scraper conveyor is the key conveying equipment for fully mechanized coal mining. Wear failure of the chute is the main form of failure of the scraper conveyor. In this study, the discrete element method (DEM) was combined with the wear model. The wear mechanism and wear regularity of the chute were explored by tracking the changes in the position of coal particles during the wear process. After the validation of wear simulation, a wear test of coal for different intrinsic parameters was designed. In one wear cycle, the three‐body wear was about 32.84 times that of the two‐body wear. In the research range, the wear of the scraper conveyor chute increased with the increase of Poisson’s ratio, shear modulus, and density of the coal. The shear modulus showed remarkable effect on the wear of the chute, followed by Poisson’s ratio and density. There existed a linear relationship between the shear modulus and wear (R² = 0.8232). This study is expected to be used to predict the wear of the scraper conveyor chute and provide a theoretical basis for the applicability of the chute in different mines.

In a fully mechanised coal-mining face, the means to train machine operators are inadequate. A remote virtual reality (VR) operation system for a fully mechanised coal-mining face using real-time data and collaborative network technology is presented in this study. The overall framework and key technologies are introduced. The system comprises three mechanical components, a LAN component based on NetworkView and RPC, access to real-time data and multifunctional human–computer interaction. The proposed system solves existing operational problems, such as training on a single PC, the lack of interactive means, poor user immersion and differences between training and actual conditions. Underground operators or students can be trained using the approaches of multi-role and virtual collaboration. Finally, front- and rear-drum drivers operated the system using the centralised control centre, the shearer remote controller and the virtual panel. The proposed method can improve the actual skills of workers and effectively reduce the risk of underground mining accidents.