Balanced multiple weighted linear discriminant analysis and its application to visual process monitoring

• 作者: Weipeng Lu Xuefeng Yan
• 来源期刊: 中国化学工程学报（英文版）
• 年，卷（期）: 2021,（8）
• 所属期刊栏目: Process Systems Engineering and Process Safety
• 页码范围: 128-137
• 页数: 10页
• 语种: 英文

摘要

Visual process monitoring is important in complex chemical processes.To address the high state separa-tion 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 discrimina-tive 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.