Incipient Fault Diagnosis of Industrial Processes Based on Residual Evaluation Multi-Feature Joint Analysis-Reference-Cited by-同舟云学术

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Incipient Fault Diagnosis of Industrial Processes Based on Residual Evaluation Multi-Feature Joint Analysis

Published:2024-07-24 Issue:31 Volume:63 Page:13692-13708
ISSN:0888-5885
Container-title:Industrial & Engineering Chemistry Research
language:en
Short-container-title:Ind. Eng. Chem. Res.

Author:

Wang Guang¹,Cao Xu¹,Chen Kaitao¹,Jiao Jianfang¹,Wang Anjie¹^ORCID

Affiliation:

1. Department of Automation, North China Electric Power University (Baoding Campus), 071003 Baoding, China

Funder

Natural Science Foundation of Hebei Province

Fundamental Research Funds for the Central Universities

National Natural Science Foundation of China

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.iecr.4c01800

Reference36 articles.

1. A survey and classification of incipient fault diagnosis approaches

2. A Novel Quality-Related Incipient Fault Detection Method Based on Canonical Variate Analysis and Kullback–Leibler Divergence for Large-Scale Industrial Processes

3. Recursive ensemble canonical variate analysis for online incipient fault detection in dynamic processes

4. Incipient Fault Detection for Complex Industrial Processes with Stationary and Nonstationary Hybrid Characteristics

5. Incipient fault detection benefited from voting fusion strategy on analysis of process variation

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