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.

A smart fully mechanized coal mining working face is comprised of various heterogeneous equipment that work together in unknown coal seam environments. The goal is to form a smart operational system with comprehensive perception, decision-making, and control. This involves many work points and complex coupling relationships, indicating it needs to be performed in stages and coordinated to address key problems in all directions and along multiple points. However, there are no existing unified test or analysis tools. Therefore, this study proposed a virtual test and evaluation method for a fully mechanized mining production system with different smart levels. This is based on the concept of “real data processing–virtual scene construction–setting key information points–virtual operation and evaluation.” The actual operational data for a specific working face geology and equipment were reasonably transformed into a visual virtual scene through a movement relationship model. The virtual operations and mining conditions of the working face were accurately reproduced. Based on the sensor and execution error analyses for different smart levels, the input interface for sensing, decision-making, and control was established for each piece of equipment, and an operation evaluation system was constructed. The system comprehensively simulates and tests the key points of sensing decision-making and control with various smart levels. The experimental results showed that the virtual scene constructed based on actual operational data has a high simulation degree. Users can simulate, analyze, and evaluate the overall operations of the smart mining 2.0–4.0 working face by inputting key information. The future direction for the smart development of fully mechanized mining is highlighted.

In this study, a real-time remote monitoring and fault diagnosis method has been developed based on the Internet of Things (IoT) frame perception, and successfully applied to a mine hoist system. The proposed method combines the sensor technology, online monitoring technology, wireless transmission technology, and fault diagnosis technology. The basic structure of the traditional IoT comprises a perception layer, a network layer, and an application layer, the proposed structure contains an additional middleware layer between the network layer and the application layer. This four-layer system is used in a mine hoist remote monitoring and fault diagnosis framework to process heterogeneous multi-source information. The sensors and parameters are connected in the perception layer, the characteristic parameters are obtained using the configuration software, and the mine local area network is saved to the data server, thereby synchronizing real-time data in the local area network. The network layer utilizes mature Internet and long-distance wireless transmission communication technologies, whereas the middleware layer comprises of a Service-Oriented Architecture (SOA)-based IoT data processing framework that integrates the multi-source heterogeneous data. Further, the fault diagnosis method is analyzed and verified based on the gray association rules. In the application layer, a human-computer interface is used for the remote monitoring and diagnosis of the mine hoist and to provide the diagnosis results as feedback to the user. The results using the aforementioned analyses are applied to the remote monitoring and diagnosis of a mine hoist system. In this study, experimental tests are conducted in this study to significantly improve the fault monitoring, diagnostic capabilities, and reliability of the mine hoist system, indicating the good application good prospects of the proposed method.

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 undulating coal seam floor ina mine leads to the complicated posture of the hydraulic supports. The existing methods for monitoring the pose of hydraulic supports have a poor monitoring effect, and are especially characterized by a lack of support position information. Therefore, this paper proposes a relative pose reconstruction method for hydraulic supports driven by digital twins. First, the pose plane and relative pose matrix of the hydraulic support are defined to describe the pose state of the support by analyzing the pose deviation between adjacent supports. Then, a digital twin system is built for a hydraulic support model, and a pose deduction method based on ray detection is proposed. The accurate position and attitude of the hydraulic support are deduced in the virtual space driven by actual laser sensor information, and three-dimensional pose reconstruction is realized. Finally, a relative pose reconstruction experiment of the hydraulic support model is designed and conducted. The results show that the relative pose data obtained by the virtual reconstruction method are very close to the experimental measurements, and the highest reconstruction accuracy is found to be 97.44%. The feasibility of the laser sensor layout scheme and the virtual reconstruction method of the pose of the hydraulic support is proved. This method provides a new way to monitor the pose of hydraulic supports, and can provide a reference for the support operation of hydraulic supports in the real fully mechanized mining process.

Current monitoring methods for hydraulic supports are designed for single machines and are unsuitable for global monitoring. This paper presents an attitude-aware method for hydraulic support groups in a virtual reality environment, which predicts the next cycle attitude in real time by a Grey–Markov prediction model. The attitude datum includes the supporting height and the corresponding attitude angles, which are obtained by installing tilt sensors on the base, the front or rear connecting rod, or the roof beam. The operation state of the hydraulic supports was determined by comparing the predicted and actual data. Next, the hydraulic support attitude was related to the coal-mining height observed through multiple cycles. The three-dimensional virtual monitoring was solved in SQL SERVER, Kingview, Unity3d, and Matlab software. Finally, the proposed method was verified on 40 hydraulic supports of a working face through 17 cycles in an underground experiment. The predictive lateral and longitudinal accuracy reached 78.9 and 82.3%, respectively. By efficiently visualizing the operation state of the hydraulic support groups in three-dimensional mode, this method can predict the future attitude with high accuracy and reliability. Therefore, it provides a theoretical approach to safe and efficient operation of a fully mechanized coal-mining face.