文献类型：期刊文献

英文题名：Fault detection based on polygon area statistics of transformation matrix identified from combined moving window data

作者：Wang, Bei[1];Yan, Xuefeng[1];Jin, Yongfei[1]

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

年份：2017

卷号：34

期号：2

起止页码：275

外文期刊名：KOREAN JOURNAL OF CHEMICAL ENGINEERING

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000394228000001)】；

基金：The authors gratefully acknowledge the support from the following foundations: 973 project of China (2013CB733600), National Natural Science Foundation of China (21176073) and the Fundamental Research Funds for the Central Universities.

语种：英文

外文关键词：Principal Component Analysis; Combined Moving Window; Polygon Area; Real-time Process Monitoring; Fault Detection

摘要：Principal component analysis (PCA) has been widely used in monitoring industrial processes, but it is still necessary to make improvements in having a timely and effective access to variation information. It is known that the transformation matrix generated from real-time PCA model indicates inner relations between original variables and new produced components, so this matrix would be different when modeling data deviate due to the change of the operating condition. Based on this theory, this paper proposes a novel real-time monitoring approach which utilizes polygon area method to measure the variation degree of the transformation matrices and then constructs a statistic for monitoring purpose. The on-line data are collected through a combined moving window (CMW), containing both normal and monitored data. To evaluate the performance of the proposed method, a simple numerical simulation, the CSTR process and the classic Tennessee Eastman process are employed for illustration, with some PCA-based methods used for comparison.