Slow‐varying batch process monitoring based on canonical variate analysis-Reference-Cited by-同舟云学术

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Slow‐varying batch process monitoring based on canonical variate analysis

Published:2022-05-04 Issue:1 Volume:101 Page:400-419
ISSN:0008-4034
Container-title:The Canadian Journal of Chemical Engineering
language:en
Short-container-title:Can J Chem Eng

Author:

Zhang Shumei¹^ORCID,Bao Xiaoli¹

Affiliation:

1. School of Electrical and Information Engineering Tianjin University Tianjin China

Funder

Natural Science Foundation of Tianjin City

National Natural Science Foundation of China

Publisher

Wiley

Subject

General Chemical Engineering

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/cjce.24401

Reference37 articles.

1. Batch process fault detection and identification based on discriminant global preserving kernel slow feature analysis

2. MoniNet With Concurrent Analytics of Temporal and Spatial Information for Fault Detection in Industrial Processes

3. Discriminant diffusion maps based k -nearest-neighbour for batch process fault detection

4. Dynamic nonlinear batch process fault detection and identification based on two‐directional dynamic kernel slow feature analysis

5. Modified canonical variate analysis based on dynamic kernel decomposition for dynamic nonlinear process quality monitoring

Cited by 3 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. Data-Driven Process Monitoring and Fault Diagnosis: A Comprehensive Survey;Processes;2024-01-24

2. Batch process monitoring based on sequential phase division multiway sparse weighted neighborhood preserving embedding;Measurement Science and Technology;2023-12-11

3. A Novel Kernel Canonical Variable Analysis Method for the AHU Fault Detection Based on the Multi-Batch Running Datasets;2023 China Automation Congress (CAC);2023-11-17

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