Image-Driven Wall-Damage Detection in Monitoring Electrical Fused Magnesia Furnace

Detecting abnormal conditions in an electrical fused magnesia furnace (EFMF) is challenging due to the difficulties in measuring ultra-high temperature, fluctuation of raw materials, uncertain boundary conditions, and numerous processing variables. In practice, images acquired from the wall of furnace are used to reflect working conditions of furnace indirectly. In this article, a supervised matrix regression algorithm is proposed to detect abnormal conditions based on the matrix data. Bilinear analysis is conducted to connect matrix data with labels and thus preserve the correlations in matrix with decreased complexity. By supervising and adjusting the weights of outliers in the modeling process, neutral and chaotic datasets can be monitored in low-dimensional spaces accurately. Moreover, a common information extraction method is proposed to eliminate redundant data. To verify proposed methods, the images recorded from an actual EFMF are used to detect abnormal conditions, and it is found that the proposed methods are able to monitor actual EFMF and issue alarms timely and effectively.