Enhanced canonical variate analysis with slow feature for dynamic process status analytics-Reference-Cited by-同舟云学术

登录注册会员服务联系我们

Enhanced canonical variate analysis with slow feature for dynamic process status analytics

Published:2020-11 Issue: Volume:95 Page:10-31
ISSN:0959-1524
Container-title:Journal of Process Control
language:en
Short-container-title:Journal of Process Control

Author:

Zheng Jiale^ORCID,Zhao Chunhui

Funder

NSFC-Zhejiang Joint Fund for the Integration of Industrialization and Informatization, China

Zhejiang Key Research and Development Project, China

State Key Laboratory of Synthetical Automation for Process Industries, China

Publisher

Elsevier BV

Subject

Industrial and Manufacturing Engineering,Computer Science Applications,Modeling and Simulation,Control and Systems Engineering

Reference43 articles.

1. Canonical variate analysis-based contributions for fault identification;Jiang;J. Process Control,2015

2. Z. Chai Z, C.H. Zhao, Enhanced random forest with concurrent analysis of static and dynamic nodes for industrial fault classification, IEEE Trans. Ind. Inf., http://dx.doi.org/10.1109/TII.2019.2915559.

3. A full-condition monitoring method for nonstationary dynamic chemical processes with cointegration and slow feature analysis;Zhao;AIChE J.,2018

4. Dynamic latent variable analytics for process operations and control;Dong;Comput. Chem. Eng.,2018

5. A novel dynamic PCA algorithm for dynamic data modeling and process monitoring;Dong;J. Process Control,2018

Cited by 33 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A novel global modelling strategy integrated dynamic kernel canonical variate analysis for the air handling unit fault detection via considering the two-directional dynamics;Journal of Building Engineering;2024-11

2. Incipient fault detection for dynamic processes with canonical variate residual statistics analysis;Chemometrics and Intelligent Laboratory Systems;2024-09

3. Enhanced dynamic latent variable analysis for dynamic process monitoring;Journal of the Taiwan Institute of Chemical Engineers;2024-03

4. SFENOSA: A Novel KPI-Related Process Monitoring Method by Slow Feature Extraction and Elastic Net Orthonormal Subspace Analysis;IEEE Transactions on Industrial Informatics;2024

5. Modified performance-enhanced PCA for incipient fault detection of dynamic industrial processes;Journal of Process Control;2023-11

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