文献类型：期刊文献

英文题名：Distributed Supervised Fault Detection and Diagnosis for a Non-Gaussian Process

作者：Tao, Yang[1];Shi, Hongbo[1];Song, Bing[1];Tan, Shuai[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：58

期号：16

起止页码：6592

外文期刊名：INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH

收录：;EI(收录号：20191906870887);WOS:【SCI-EXPANDED(收录号:WOS:000466053500039)】；

基金：This research is supported by the National Natural Science Foundation of China (61703161, 61673173), Fundamental Research Funds for the Central Universities (222201714031), and China Postdoctoral Science Foundation (2017M611472).

语种：英文

外文关键词：Numerical methods - Gaussian distribution - Fault detection - Regression analysis

摘要：In this paper, a novel method named distributed independent component-principal component regression (distributed ICPCR) is proposed to monitor the large-scale non-Gaussian process. First, multiple sub-blocks are obtained, and the key process variables are selected for the following distribution monitoring. Second, the monitoring model of each sub-block is constructed on the basis of the proposed ICPCR method. In this algorithm, the total latent space which contains both Gaussian and non-Gaussian characteristics is constructed, and the regression model between the total latent variables and quality variables is established for the quality-related monitoring. Afterward, the global monitoring result can be obtained by the Bayesian fusion strategy. Third, a probability-based method is proposed to determine the fault sub-blocks, and the ICPCR-based relative contribution plot is presented to locate the fault variables. Finally, a numerical example and the Tennessee Eastman process are used to demonstrate the effectiveness of the proposed method.