Multimode complex process monitoring using double-level local information based local outlier factor method-Reference-Cited by-同舟云学术

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Multimode complex process monitoring using double-level local information based local outlier factor method

Published:2018-06-05 Issue:10 Volume:32 Page:e3048
ISSN:0886-9383
Container-title:Journal of Chemometrics
language:en
Short-container-title:Journal of Chemometrics

Author:

Wang Lei¹^ORCID,Deng Xiaogang¹,Cao Yuping¹

Affiliation:

1. College of Information and Control Engineering; China University of Petroleum; Qingdao 266580 China

Funder

National Natural Science Foundation of China

China University of Petroleum, Beijing

Natural Science Foundation of Shandong Province, China

Shandong Provincial Key Programme of Research & Development

Publisher

Wiley

Subject

Applied Mathematics,Analytical Chemistry

Link

http://onlinelibrary.wiley.com/wol1/doi/10.1002/cem.3048/fullpdf

Reference42 articles.

1. Review of recent research on data-based process monitoring;Ge;Ind Eng Chem Res,2013

2. Data mining and analytics in the process industry: the role of machine learning;Ge;IEEE Access,2017

3. Statistical monitoring of wastewater treatment plants using variational Bayesian PCA;Liu;Ind Eng Chem Res,2014

4. Statistical process monitoring with integration of data projection and one-class classification;Liu;Chemom Intel Lab Syst,2015

5. Fault discriminant enhanced kernel principal component analysis incorporating prior fault information for monitoring nonlinear processes;Deng;Chemom Intel Lab Syst,2017

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1. Complex Multimode Process Monitoring Based on the Improved Canonical Variable Analysis;2022 34th Chinese Control and Decision Conference (CCDC);2022-08-15

2. Deep model based on mode elimination and Fisher criterion combined with self-organizing map for visual multimodal chemical process monitoring;Information Sciences;2021-07

3. A Multimode Anomaly Detection Method Based on OC-ELM for Aircraft Engine System;IEEE Access;2021

4. Recursive Gaussian Mixture Models for Adaptive Process Monitoring;Industrial & Engineering Chemistry Research;2019-04-01

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