文献类型：会议论文

英文题名：Joint Probability Density and Weighted Probabilistic PCA Based on Coefficient of Variation for Multimode Process Monitoring

作者：Zhu, Tian-xian[1];Huang, Jian[1];Yan, Xue-feng[1]

机构：[1]East China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China

会议论文集：International Conference on Artificial Intelligence - Techniques and Applications (AITA)

会议日期：SEP 25-26, 2016

会议地点：Shanghai, PEOPLES R CHINA

语种：英文

外文关键词：Multimode process monitoring; Joint probability; Weighted probabilistic PCA; Coefficient of variation

摘要：For probabilistic monitoring of multimode processes, this paper introduced a monitoring scheme that integrates joint probability density and weighted probabilistic principal component analysis based on coefficient of variation (CV-WPPCA). A joint probability based on T-2 statistic was constructed for mode identification. After it concentrated maximum fault-relevant information into dominant subspace by identifying and extracting important noise factors from the residual subspace, the new approach utilized a weighting strategy based on coefficient of variation method to highlight the useful information in the reconstructed dominant subspace. A case study on the Tennessee Eastman process was applied to demonstrate the efficiency of the proposed method.