Updating strategy of safe operation control model for dense medium coal preparation process based on Bayesian network and incremental learning

Abstract

The effectiveness of control decisions provided by the safe operation control model for the dense medium coal preparation process may decline due to its inability to adapt to changing working conditions. To address this issue, this paper investigates a safe operation control model update strategy based on Bayesian network and incremental learning. This strategy can update the model structure and parameters according to different conditions, ensuring the effectiveness of the updated model. Considering that the old model has effective information to explain the new working conditions, the Bayesian network structure update learning method based on incremental learning is proposed. This method retains the components of the old model that can describe the joint probability distribution of the sampled data under the new working conditions while updating the remaining structure. This approach improves the efficiency of model updating. The simulation results show that the updated model obtained by the proposed method can effectively deal with new abnormal conditions.