C-IPLS–IKPLS for Modeling and Detecting Nonlinear Multimode Processes-Reference-Cited by-同舟云学术

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C-IPLS–IKPLS for Modeling and Detecting Nonlinear Multimode Processes

Published:2021-01-22 Issue:4 Volume:60 Page:1684-1698
ISSN:0888-5885
Container-title:Industrial & Engineering Chemistry Research
language:en
Short-container-title:Ind. Eng. Chem. Res.

Author:

Sun Rongrong¹^ORCID,Wang Youqing¹^ORCID

Affiliation:

1. College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China

Funder

Natural Science Foundation of Shandong Province

National Natural Science Foundation of China

Program for Entrepreneurial and Innovative Leading Talents of Qingdao

Publisher

American Chemical Society (ACS)

Subject

Industrial and Manufacturing Engineering,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.iecr.0c04038

Reference45 articles.

1. KPI relevant and irrelevant fault monitoring with neighborhood component analysis and two-level PLS

2. Data-Driven Process Monitoring Based on Modified Orthogonal Projections to Latent Structures

3. Performance monitoring of a multi-product semi-batch process

4. Real-time monitoring for a process with multiple operating modes

5. On-line multivariate statistical monitoring of batch processes using Gaussian mixture model

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1. Key-Performance-Indicator-Related Fault Detection Based on Deep Orthonormal Subspace Analysis;IEEE Transactions on Industrial Informatics;2024-05

2. Data-Driven Process Monitoring and Fault Diagnosis: A Comprehensive Survey;Processes;2024-01-24

3. A new monitoring approach of time-varying and nonlinear processes with application to penicillin fermentation process;Journal of Intelligent & Fuzzy Systems;2023-10-04

4. A transfer learning approach using improved copula subspace division for multi‐mode fault detection;The Canadian Journal of Chemical Engineering;2023-05-19

5. Improved key performance indicator-partial least squares method for nonlinear process fault detection based on just-in-time learning;Journal of the Franklin Institute;2023-01

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