Online Weighted Slow Feature Analysis Based Fault Detection Algorithm

被引：0

作者：

Huang J. ^{[1
]}

Yang X. ^{[1
]}

机构：

[1] School of Automation and Electrical Engineering, Key Laboratory of Knowledge Automation for Industrial Processes of the Ministry of Education, University of Science and Technology Beijing, Beijing

来源：

Shanghai Jiaotong Daxue Xuebao/Journal of Shanghai Jiaotong University | 2020年 / 54卷 / 11期

关键词：

Chemical process; Dynamic process; Fault detection; Slow feature analysis (SFA); Weighted feature;

D O I：

10.16183/j.cnki.jsjtu.2020.99.012

中图分类号：

学科分类号：

摘要：

In industrial process monitoring, traditional process monitoring methods fail to extract process dynamic information and the online fault-related information is not fully utilized in feature selection. To solve these problems, a fault detection method based on online weighted slow feature analysis (OWSFA) is proposed. First, the slow feature analysis(SFA)algorithm is utilized to extract the dynamic features. The threshold of slow feature is estimated based on benchmark data. The online features which exceed the threshold based on the slack factor are selected as the suspected fault features. Then, the monitoring statistics are built based on the weighted suspected fault features by introducing the weights. The proposed OWSFA algorithm is applied in a numerical system and the Tennessee Eastman process, which proves that the proposed method has superiorities compared with principal component analysis and the SFA method. According to the faulty information, the OWSFA algorithm generates the online weighted statistics to enhance the fault features in the monitoring model. © 2020, Shanghai Jiao Tong University Press. All right reserved.

引用

页码：1142 / 1150

页数：8

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