Abstract
To improve the performance of the abnormal condition identification, the multi-source information of the abnormal conditions in the electro-fused magnesia smelting process is analyzed in this paper. An intelligent abnormal condition identification method is proposed based on Bayesian network (BN). By analyzing three main abnormal conditions and the experience of the operators, the characteristics related with the abnormal conditions are extracted. The BNs are established to identify the abnormal conditions by fusing the multi-source information. The simulation results show that the proposed method can realize abnormal condition identification, distinguish the degree of the abnormal condition, and obtain better performance.