文献类型：期刊文献

英文题名：Quality-Related Fault Detection Based on Improved Independent Component Regression for Non-Gaussian Processes

作者：Aljunaid, Majed[1];Shi, Hongbo[1];Tao, Yang[1]

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

年份：2019

卷号：7

起止页码：158594

外文期刊名：IEEE ACCESS

收录：;EI(收录号：20200408061487);WOS:【SCI-EXPANDED(收录号:WOS:000495681500001)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 61703161and Grant 61673173, and in part by the Fundamental Research Funds for the Central Universities under Grant 222201714031.

语种：英文

外文关键词：Quality-related fault detection; independent component regression; orthogonal signal correction; non-Gaussian process; QR decomposition

摘要：Partial least squares (PLS) and linear regression methods have been widely utilized for quality-related fault detection in industrial processes recently. Since these traditional approaches assume that process variables follow Gaussian distribution approximately, their effectiveness will be challenged when facing non-Gaussian processes. To deal with this difficulty, a new quality relevant process monitoring approach based on improved independent component regression (IICR) is presented in this article. Taking high-order statistical information into account, ICA is performed onto process data to produce independent components (ICs). In order to remove irrelevant variation orthogonal to quality variable and keep as much quality-related fault information as possible, a new quality-related independent components selection method is applied to these ICs. Then the regression relationship between filtered ICs and the product quality is built. QR decomposition for regression coefficient matrix is able to give out quality-related and quality-unrelated projectors. After the measured variable matrix is divided into quality relevant and quality irrelevant parts, novel monitoring indices are designed for fault detection. finally, applications to two simulation cases testify the effectiveness of our proposed quality-related fault detection method for non-Gaussian processes.