文献类型：期刊文献

英文题名：Fault Detection and Diagnosis in Chemical Processes Using Sensitive Principal Component Analysis

作者：Jiang, Qingchao[1];Yan, Xuefeng[1];Zhao, Weixiang[2]

机构：[1]E China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]Natl Grid, Hicksville, NY 11801 USA

年份：2013

卷号：52

期号：4

起止页码：1635

外文期刊名：INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

收录：;EI(收录号：20130615986975);WOS:【SCI-EXPANDED(收录号:WOS:000314492200029)】；

基金：The authors gratefully acknowledge the supports from the following foundations: National Natural Science Foundation of China (21176073), Doctoral Fund of Ministry of Education of China (20090074110005), Program for New Century Excellent Talents in University (NCET-09-0346), "Shu Guang" project (09SG29), 973 project (2012CB721006), and the Fundamental Research Funds for the Central Universities.

语种：英文

外文关键词：Process control - Process monitoring - Chemical detection - Fault detection

摘要：Sensitive principal component analysis (SPCA) is proposed to improve the principal component analysis (PCA) based chemical process monitoring performance, by solving the information loss problem and reducing nondetection rates of the T-2 statistic. Generally, principal components (PCs) selection in the PCA-based process monitoring is subjective, which can lead to information loss and poor monitoring performance. The SPCA method is to subsequently build a conventional PCA model based on normal samples, index PCs which reflect the dominant variation of abnormal observations, and use these sensitive PCs (SPCs) to monitor the process. Moreover, a novel fault diagnosis approach based on SPCA is also proposed due to SPCs' ability to represent the main characteristic of the fault. The case studies on the Tennessee Eastman process demonstrate the effect of SPCA on online monitoring, showing its performance is significantly better than that of the classical PCA methods.