HoStB-DVGNN: A Flotation Fault Recognition Method Using Higher Order Spatial--Temporal Block and Dual-Stream Variational GNN

Accurate fault pattern recognition in the flotation process is crucial for rapid fault response and reducing production risks. However, dynamic flotation process data exhibit high dimensionality, nonlinearity, and nonstationarity, along with significant noise and uncertainty, which severely impact the accuracy (ACC) of fault pattern recognition. To address these challenges, we propose a fault pattern recognition method for froth flotation based on higher order spatial-temporal block and dual-stream variational graph neural networks (HoStB-DVGNNs). First, we construct a high-order spatiotemporal block using the groups of key frame images to comprehensively characterize the flotation production conditions. Then, we develop a dual-stream variational graph neural network (DVGNN) that includes an apparent feature stream and HoStB stream to effectively extract dynamic froth information. In addition, we introduce a bilateral self-supervision mechanism to build a variational autoencoder (VAE), significantly enhancing the model's generalization performance. Finally, extensive experiments on benchmark datasets and real flotation processes validate the effectiveness and robustness of the proposed fault pattern recognition method.