Structured discriminative Gaussian graph learning for multimode process monitoring

Author:

Wang Jing1,Liu Yi2ORCID,Zhang Dongping3,Xie Lei4,Zeng Jiusun5

Affiliation:

1. College of Metrology and Measurement Engineering China Jiliang University Hangzhou China

2. School of Information Science and Technology Hangzhou Normal University Hangzhou China

3. College of Information Engineering China Jiliang University Hangzhou China

4. Institute of Cyber Systems and Control Zhejiang University Hangzhou China

5. School of Mathematics Hangzhou Normal University Hangzhou China

Abstract

AbstractAiming at the actual industrial process background that different modes share the same system configurations and control structure, this article proposes a novel structured discriminant Gaussian graph learning for multimode process monitoring. The proposed method considers not only the sparsity of graph model but also the measurement of data variation based on a mismatched graph and the common node support between different graphical structures. The objective function involves two sets of regularization terms: the trace terms for mismatched measurements and the ‐norm imposed on the union of decomposed graph matrices. Due to the introduced mismatched trace terms, the cost of matching the data points and graph models that have inconsistent class labels can be expanded, which brings more discrimination for the graph‐based mode identification. While the common structure extracted by the ‐norm forces the estimated graph models to have structural similarities, thus alleviating the negative influence caused by graph discrimination. Once a relatively accurate and discriminative reference graph model is obtained, the downstream test graph learning and analysis can be conducted online by employing the moving window techniques. By comparing the matched and mismatched graph‐based measurements, the process mode can be identified correctly and stably. To grasp the abnormal process changes, the ‐norm for the row sparsity is again applied to the graph difference matrices, the sensitive monitoring statistics and the fault isolation results can be obtained effectively. All the optimization problems in this paper can be solved using the alternating direction multiplier (ADMM) algorithm. The effectiveness of our proposed approach is illustrated by the application to a real blast furnace iron‐making production process.

Funder

Key Research and Development Program of Zhejiang Province

National Natural Science Foundation of China

Natural Science Foundation of Zhejiang Province

Publisher

Wiley

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3