Euclidean-Riemannian Joint Low-Rank Projections for Industrial Process Monitoring-Reference-Cited by-同舟云学术

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Euclidean-Riemannian Joint Low-Rank Projections for Industrial Process Monitoring

Published:2024-03-19 Issue:13 Volume:63 Page:5803-5812
ISSN:0888-5885
Container-title:Industrial & Engineering Chemistry Research
language:en
Short-container-title:Ind. Eng. Chem. Res.

Author:

Fu Yuanjian¹,Luo Chaomin²,Xu Xue³^ORCID

Affiliation:

1. School of Control Science and Engineering, Tiangong University, Tianjin 300387, China

2. Department of Electrical and Computer Engineering, Mississippi State University, Mississippi State, Mississippi 39762, United States

3. School of Artificial Intelligence, Tiangong University, Tianjin 300387, China

Funder

National Natural Science Foundation of China

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.iecr.3c04244

Reference43 articles.

1. Process data analytics in the era of big data

2. Non-Gaussian Quality Relevant Process Monitoring Based on Higher-Order Statistics Projection to the Latent Structure and Independent Signal Correction

3. Stationary Subspace Analysis-Based Hierarchical Model for Batch Processes Monitoring

4. Fault Detection for Nonlinear Process With Deterministic Disturbances: A Just-In-Time Learning Based Data Driven Method

5. Topology-Guided Graph Learning for Process Fault Diagnosis

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