Process monitoring algorithm based on fault-related slow feature analysis

被引：0
|
作者：
Huang J. [1 ]
Yang X. [1 ]
Chen X.-Z. [1 ]
机构：
[1] Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing
来源：
Gao Xiao Hua Xue Gong Cheng Xue Bao/Journal of Chemical Engineering of Chinese Universities | 2020年 / 34卷 / 05期
关键词：
Dynamic process; Fault detection; Fault-related feature; Slow feature analysis;
D O I：
10.3969/j.issn.1003-9015.2020.05.026
中图分类号：
O212 [数理统计];
学科分类号：
摘要：
Because the slow feature analysis (SFA) algorithm does not utilize online fault information in feature selection, a fault detection algorithm based on the selection of online fault-related feature (FRSFA) was proposed. Firstly, the SFA algorithm was used to extract the dynamic features of the process, and the kernel density estimation method was used to estimate the average threshold of the features as the reference for online feature selection. Then the online slow features exceeding the average threshold were recorded as the fault-related features. The current fault-related features were selected from the dynamic features of normal data, and the control limit of the current monitoring sample was estimated. Finally, the proposed algorithm was applied to Tennessee Eastman process. The results show that compared with principal component analysis and SFA algorithms, FRSFA can fully use the online fault information of dynamic process and enhance the effectiveness of the model. © 2020, Editorial Board of "Journal of Chemical Engineering of Chinese Universities". All right reserved.
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页码：1290 / 1296
页数：6
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