Fault detection method based on variable sub-region PCA

被引：0

作者：

Wang L. ^{[1
]}

Deng X. ^{[1
]}

Xu Y. ^{[1
]}

Zhong N. ^{[1
]}

机构：

[1] College of Information and Control Engineering, China University of Petroleum, Qingdao, 266555, Shandong

来源：

Huagong Xuebao/CIESC Journal | 2016年 / 67卷 / 10期

基金：

中国国家自然科学基金;

关键词：

Bayesian inference; Dynamic simulation; Fault detection; Principal component analysis; Process systems; Variable sub-region;

D O I：

10.11949/j.issn.0438-1157.20160217

中图分类号：

学科分类号：

摘要：

Aiming at the problem that traditional principal component analysis (PCA) method can't highlight the local variable information in industrial process monitoring, this paper proposes a variable sub-region PCA (VSR-PCA) fault detection method. First, PCA is used to decompose original data space into principal component subspace (PCS) and residual subspace (RS), and mutual information between variables and PCS is calculated to measure their correlation which is utilized to obtain the variable sub-regions. Then, local T2 statistics and local SPE statistics are calculated in each variable sub-region. Bayesian inference is applied to integrate information in every sub-region to construct global statistics which are able to emphasize the local variable information while preserving the whole process information. Simulation results on the continuous stirred tank reactor (CSTR) system show that VSR-PCA method has better process monitoring performance. © All Right Reserved.

引用

页码：4300 / 4308

页数：8

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