文献类型：期刊文献

英文题名：Double-step block division plant-wide fault detection and diagnosis based on variable distributions and relevant features

作者：Huang, Jian[1];Yan, Xuefeng[1]

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

年份：2015

卷号：29

期号：11

起止页码：587

外文期刊名：JOURNAL OF CHEMOMETRICS

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000368247100003)】；

基金：The authors gratefully acknowledge the support from the following foundations: 973 project of China (2013CB733600), National Natural Science Foundation of China (21176073), Program for New Century Excellent Talents in University (NCET-09-0346), and the Fundamental Research Funds for the Central Universities.

语种：英文

外文关键词：distribution characteristic; relevant feature; plant-wide process monitoring; PCA; ICA

摘要：Large-scale process data in plant-wide process monitoring are characterized by two features: complex distributions and complex relevance. This study proposes a double-step block division plant-wide process monitoring method based on variable distributions and relevant features to overcome this limitation. First, the data distribution is considered, and the normality test method called the D-test is applied to classify the variables with the same distribution (i.e., Gaussian distribution or non-Gaussian distribution) in a block. Thus, the second block division is implemented on both blocks obtained in the previous step. The mutual information shared between two variables is used to generate relevant matrixes of the Gaussian and non-Gaussian blocks. The K-means method clusters the vectors of the relevant matrix. Principal component analysis is conducted to monitor each Gaussian subblock, whereas independent component analysis is conducted to monitor each non-Gaussian subblock. A composite statistic is eventually derived through Bayesian inference. The proposed method is applied to a numerical system and the Tennessee Eastman process data set. The monitoring performance shows the superiority of the proposed method. Copyright (C) 2015 John Wiley & Sons, Ltd.