Impact-slip experiments and systematic study of coal gangue “category” recognition technology part II: Improving effect of the proposed parallel voting system method on coal gangue “category” recognition accuracy based on impact-slip experiments

Abstract

The research on coal gangue “category” recognition via single classification algorithm for single-channel signal data shows that the classification and recognition capabilities of different algorithms on different channel signals and data types exhibit significant differences. In order to improve coal gangue classification and recognition effect, a coal gangue classification and recognition method of “multi-information fusion” based on the “parallel voting system (PVS)” was proposed in this study. The joint judgment of coal gangue “category” was completed through the combination of multiple algorithms or channel signal data with stronger recognition ability according to the voting principle of “the minority is subordinate to the majority”. Based on the signal characteristics data obtained from the impact-slip test between coal gangue and the hydraulic support, three different modes of PVS-based coal gangue classification and recognition, namely, channel signal data PVS (SDPVS), algorithm PVS (APVS), and algorithm and channel signal data combination double PVS (algorithm-data double parallel, A&SDPVS), were respectively investigated herein. The influence of signals combination mode, algorithms matching mode as well as combined mode of signals and algorithms on coal gangue “category” voting recognition accuracy was studied. The research results show that for the SDPVS when a single algorithm with higher recognition accuracy and its corresponding multiple channel signal data with higher recognition accuracy were selected and for the A&SDPVS when the multiple “Recognition combination of single algorithm and single channel signal data” with higher recognition accuracy was selected, coal gangue classification and recognition effect could be improved. PVS-based coal gangue “category” recognition accuracy could reach up to 97.5%, which effectively improved coal gangue classification and recognition ability; moreover, the effectiveness of the multi-information fusion method proposed in this study was proven.