Robust semi-supervised modelling method and its application to fault detection in chemical processes

被引：0
|
作者：
Zhou L. [1 ]
Song Z. [2 ]
Hou B. [1 ]
Fei Z. [1 ]
机构：
[1] School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Hangzhou, 310024, Zhejiang
[2] College of Control Science and Engineering, Zhejiang University, Hangzhou, 310027, Zhejiang
来源：
Huagong Xuebao/CIESC Journal | 2017年 / 68卷 / 03期
基金：
中国国家自然科学基金;
关键词：
Fault detection; Principal component analysis; Process control; Process systems; Robust model; Semi-supervised learning;
D O I：
10.11949/j.issn.0438-1157.20161205
中图分类号：
学科分类号：
摘要：
In most complex chemical processes, measurements are often collected with noises and some outliers. These contaminated data would have negative effect on the accuracy of data-based process modelling and fault detection. A new robust semi-supervised PLVR model (RSSPLVR) was proposed by consideration of the real measuring environment in chemical processes and extended to a nonlinear model K-RSSPLVR with a kernel methodology. In both RSSPLVR and K-RSSPLVR, a weighted coefficient based on sample similarity among all observations was used as prior checking parameter of probability model to effectively eliminate influence of outliers on modelling. Model parameter training was accomplished by analysis of the weighted dataset with EM algorithm and a fault detection scheme was developed. Finally, TE process simulation demonstrated effectiveness of the proposed modelling methods. © All Right Reserved.
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页码：1109 / 1115
页数：6
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