Hypergraph Convolution Rebalancing Cascade Broad Learning for Coal-Rock Cutting State Recognition

Coal-rock cutting state (CRCS) recognition plays an important role in improving shearer automation and ensuring the safe and efficient mining of coal mines. However, due to the randomness and unevenness of the distribution of underground coal seams, the vibration monitoring data of different cutting states are usually imbalanced and exhibit complex structural relationships. Therefore, this article novelty proposes a hypergraph convolution rebalancing cascade broad learning (HCRCBL) model for CRCS recognition. Specifically, HCRCBL recasts the cascaded feature mapping nodes of the original cascade broad learning system (CBLS) through a random hypergraph convolution mechanism to fully leverage the high-order relationships among cutting vibration data. Meanwhile, according to the data prior distribution, a rebalancing weight assignment strategy is designed for HCRCBL to pay more attention to the minority class and low-density distribution samples, which enhances the CRCS recognition performance of imbalanced data. Besides, HCRCBL adopts the ridge regression method to obtain the global optimal solution, assuring its high computation efficiency. The effectiveness of the HCRCBL-based CRCS recognition framework is demonstrated via the monitoring data from a coal-rock cutting experiment platform of shearer. The comparison results verify that the proposed method achieves high recognition accuracy while costing low training time consumption and effectively handling imbalanced data.