A Novel Coal-Gangue Recognition Method for Top Coal Caving Face Based on IALO-VMD and Improved MobileNetV2 Network

Abstract

Accurate and rapid recognition of coal and gangue is an important prerequisite for safe and efficient mining in top coal caving face. In this paper, a novel coal-gangue recognition method is put forward based on improved ant-lion optimization algorithm (IALO), variational modal decomposition (VMD) and improved MobileNetV2 network. Firstly, two strategies of trap boundary adjustment and chaotic mapping are designed for ALO to sufficiently explore the solution space and prevent falling into local optimization. Subsequently, IALO is employed to search the optimal parameters of VMD and the superiority of IALO-VMD can be reasonably embodied through some simulation analysis. Then, the vibration signal-image mapping is performed to produce rich sample data for MobileNetV2. The coordinate attention mechanism and Inception structure are combined with MobileNetV2 to accelerate the training speed and improve the classification accuracy, and the improved MobileNetV2 based classifier is constructed to fulfill an automatic coal-gangue recognition. Finally, an experimental platform of coal-gangue impacting the tail beam of hydraulic support is built and many comparison experiments are carried out. The experiment results indicate that the proposed coal-gangue recognition method has the prediction accuracy of 99.66%, which is increased by 1.43% compared to the classical MobileNetV2. The underground on-site testing results show that the average prediction accuracy of the proposed coal-gangue recognition model can exceed 93%, and can effectively and accurately distinguish different top coal caving states.