Multimode process monitoring based on data-driven method

In this paper, a new data-driven method and its application to process monitoring is proposed for handling the multimode process monitoring problem in the electro fused magnesia furnace (EFMF). Compared to conventional methods, the contributions are as follows: (1) New similarity between different mode is defined with weighted norm distance which can extract common and special feature of all modes respectively, and the similar degree is analyzed; (2) Multi-mode modeling method is then proposed based on the similarity defined above; (3) Fault caused by different section often performs abnormal in different subspace, so we applied the fault detecting indices with the multi-mode model. The experiment results show the effectiveness of the proposed method.