Non-Gaussian Quality Relevant Process Monitoring Based on Higher-Order Statistics Projection to the Latent Structure and Independent Signal Correction-Reference-Cited by-同舟云学术

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

Published:2023-01-30 Issue:6 Volume:62 Page:2777-2791
ISSN:0888-5885
Container-title:Industrial & Engineering Chemistry Research
language:en
Short-container-title:Ind. Eng. Chem. Res.

Author:

Zhou Jinglin¹^ORCID,Yang Zhongyi¹^ORCID

Affiliation:

1. College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, P. R. China

Funder

National Natural Science Foundation of China

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.2c03588

Reference35 articles.

1. Robust Multivariate Statistical Process Monitoring via Stable Principal Component Pursuit

2. A Novel Dynamic Weight Principal Component Analysis Method and Hierarchical Monitoring Strategy for Process Fault Detection and Diagnosis

3. An Approach on MCSA-Based Fault Detection Using Independent Component Analysis and Neural Networks

4. Statistical process monitoring with independent component analysis

5. Ren, M.; Liang, Y.; Gong, M. An Improved PCA-based Fault Detection Method for non-Gaussian Systems Using SIP Criterion. IEEE 15th International Conference on Control and Automation (ICCA): Edinburgh, 2019; p 905.

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