Visual process monitoring is important in complex chemical processes. To address the high state separation of industrial data

we propose a new criterion for feature extraction called balanced multiple weighted linear discriminant analysis (BMWLDA). Then

we combine BMWLDA with self-organizing map (SOM) for visual monitoring of industrial operation processes. BMWLDA can extract the discriminative feature vectors from the original industrial data and maximally separate industrial operation states in the space spanned by these discriminative feature vectors. When the discriminative feature vectors are used as the input to SOM

the training result of SOM can differentiate industrial operation states clearly. This function improves the performance of visual monitoring. Continuous stirred tank reactor is used to verify that the class separation performance of BMWLDA is more effective than that of traditional linear discriminant analysis

approximate pairwise accuracy criterion

max-min distance analysis

maximum margin criterion

and local Fisher discriminant analysis. In addition

the method that combines BMWLDA with SOM can effectively perform visual process monitoring in real time.