Two-dimensional multiphase batch process monitoring based on sparse canonical variate analysis-Reference-Cited by-同舟云学术

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Two-dimensional multiphase batch process monitoring based on sparse canonical variate analysis

Published:2022-08 Issue: Volume:116 Page:185-198
ISSN:0959-1524
Container-title:Journal of Process Control
language:en
Short-container-title:Journal of Process Control

Author:

Zhang Shumei^ORCID,Bao Xiaoli^ORCID

Funder

State Key Laboratory of Industrial Control Technology

Natural Science Foundation of Tianjin Science and Technology Correspondent Project

National Natural Science Foundation of China

Zhejiang University

Natural Science Foundation of Tianjin City

Publisher

Elsevier BV

Subject

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

Reference38 articles.

1. Multiphase two-dimensional time-slice dynamic system for batch process monitoring;Zhu;J. Process Control.,2020

2. Multivariate statistical process monitoring of batch-to-batch startups;Yan;AlChE J.,2015

3. Pharmaceutical quality by design: product and process development, understanding, and control;Lawrence;Pharm. Res.,2008

4. Slow-feature-analysis-based batch process monitoring with comprehensive interpretation of operation condition deviation and dynamic anomaly;Zhang;IEEE Trans. Ind. Inf.,2019

5. Dynamic process fault detection and diagnosis based on a combined approach of hidden Markov and Bayesian network model;Don;Chem. Eng. Sci.,2019

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

3. Automatic segmentation of dynamic and static models based on high order slow feature analysis and principal component analysis for multiphase batch monitoring;Expert Systems with Applications;2024-08

4. Nonlinear Quality‐Related Fault Detection Using Neighborhood Embedding Neural Orthogonal Mapping Algorithm for Batch Process;Chemical Engineering & Technology;2024-03-30

5. Attention-Based Two-Dimensional Dynamic-Scale Graph Autoencoder for Batch Process Monitoring;Processes;2024-03-02

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