Abnormal Condition Identification Modeling Method Based on Bayesian Network Parameters Transfer Learning for the Electro-Fused Magnesia Smelting Process

When the data of target domain are scarce, the established model will not be accurate enough to analyze the target problem. For the abnormal condition identification modeling problem of electro-fused magnesia smelting process, this paper proposes the new Bayesian network (BN) parameters transfer learning method based on the expert knowledge from target domain to increase the accuracy of abnormal condition identification. First of all, the electro-fused magnesia smelting process is introduced and the existing research results on the abnormal condition identification are analyzed. The problem to solve in this paper is described. Furthermore, the constraints from expert knowledge for the target model are shown in two forms. The new BN parameters transfer learning method is proposed. Finally, the proposed method is verified by the Asia network, and it is applied to establish the abnormal exhausting condition identification model for the electro-fused magnesia smelting process. The simulation results demonstrate the effectiveness of proposed method which owns the better performances.