LAUSR

A complex industrial process is dynamic and unpredictable, and it is impractical to prevent all abnormal conditions. In practice, the occurrence, development, and elimination of abnormal conditions take different states in different views. Traditional single-view-based methods fail to monitor all types of abnormal conditions, which can easily be disturbed and rendered useless due to the complexity and variability of industrial sites, and there is an emerging need to advance a condition monitoring system based on multiview data. In this article, collaborative feature regression (CFR) is proposed to process multiview data in detecting abnormal working conditions, and its novelties include: 1) a unified framework to correspond both of physical variables and image matrix to the working conditions of an industrial process; 2) a new data preprocessing method to convert multiview data into the data with the same type for further consideration; and 3) a holistic feature regression model using multiview data in which sample points in are weighted based on their qualities in classifications. The above CFR-based method is implemented to classify working conditions of industrial processes and detect abnormal conditions. Compared with the single-view-based methods, the proposed method can identify abnormal conditions in a timely and effective manner, ensuring the stable operation of industrial processes. Finally, its effectiveness is proven when it is used to detect a sequence of abnormal working conditions in an electro-fused magnesia furnace (EFMF).