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.
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.