文献类型：期刊文献

英文题名：Quality-related fault detection based on mutual information principal component analysis

作者：Zhao, Shuai[1]; Song, Bing[1]; Shi, Hongbo[1]

机构：[1] Key Laboratory of Advanced Control and Optimization for Chemical Processes, East China University of Science and Technology, Ministry of Education, Shanghai, 200237, China

年份：2017

起止页码：4163

外文期刊名：Proceedings of the 29th Chinese Control and Decision Conference, CCDC 2017

收录：EI(收录号：20173504090460)

语种：英文

摘要：Quality-related fault detection has received extensive attention in recent years. It requires an appropriate supervisory relationship between process variables and quality variables. While the traditional principal component analysis (PCA) doesn't consider the relationships between them. Thus we proposed the mutual information principal component analysis (MIPCA) to detect the quality-related faults. MIPCA fully integrates the advantages of mutual information (MI) and PCA. With MIPCA, process variables can be utilized to monitor the process under the supervision of quality variables and judge a fault is whether related to the quality or not. Finally, the feasibility and effectiveness of the MIPCA are verified in Tennessee Eastman Process (TEP). ? 2017 IEEE.