Adaptive fault detection method based on correlation analysis of independent component

被引：0

作者：

Wang P.-L. ^{[1
,2
]}

Ye X.-F. ^{[2
]}

Yang Z.-Y. ^{[2
]}

机构：

[1] School of Engineering, Huzhou University, Huzhou, 313000, Zhejiang

[2] College of Electronic and Information, Hangzhou Dianzi University, Hangzhou, 310018, Zhejiang

来源：

Kongzhi Lilun Yu Yingyong/Control Theory and Applications | 2018年 / 35卷 / 09期

基金：

中国国家自然科学基金;

关键词：

Adaptive factors; Correlation; evaluation; Fault detection; Hidden Markov model; Independent component analysis; Particle swarm optimization algorithm;

D O I：

10.7641/CTA.2018.70725

中图分类号：

学科分类号：

摘要：

Industrial process data has dynamic, non-Gauss characteristics. The independent component analysis (ICA) not only can analysis the non-gauss data, but also be able to remove the coupling among the multi-variables and meet the independent requirement. The particle swarm optimization (PSO) algorithm is introduced in this paper to optimize the parameters of ICA model, and the independent components are adaptively determined by negative entropy maximization. Meanwhile, a new adaptive detecting limit based on hidden Markov model (HMM) is put forward to monitor the industrial process, which pays attention to the change of feature information in correlated data blocks. In this proposed method, the independent components in a time window are seemed as the basic unit to train the HMM model, which is contributes to describe correlation evaluation information. With the evaluation value, an adaptive factor and detecting limitation which are based on the acceptable margin are designed to track the change of feature information and monitor the process online dynamically. Finally, the experimental results of the Tennessee Eastman (TE) simulation platform show the effectiveness of the proposed method. © 2018, Editorial Department of Control Theory & Applications South China University of Technology. All right reserved.

引用

页码：1331 / 1338

页数：7

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