Learning Local Gabor Pattern-Based Discriminative Dictionary of Froth Images for Flotation Process Working Condition Monitoring

This article presents a simple yet powerful online flotation process working condition (FPWC) discrimination approach based on the sparse representation of froth images. It learns a local Gabor pattern-based discriminative dictionary with a linear classification model simultaneously for the FPWC identification by solving a sparsity-constrained optimization problem. The proposed method tends to achieve similar and distinct sparse codes of froth images for the same and different FPWCs, respectively, facilitating the accurate FPWC identification. To ensure the adaptability of the FPWC discrimination model, an incremental learning-based online model updating procedure is further derived to monitor the dynamically changing characteristics of FPWCs based on an introduced sparsity discrimination index. The proposed method was validated on an industrial preferential lead-flotation subcircuit process. The prototype monitoring system with extensive confirmatory and comparative experiments shows the effectiveness and superiority of the proposed method, which lays a foundation for the optimal control of industrial flotation processes.