F-Norm-Based Soft LDA Algorithm for Fault Detection in Chemical Production Processes-Reference-Cited by-同舟云学术

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F-Norm-Based Soft LDA Algorithm for Fault Detection in Chemical Production Processes

Published:2024-08-01 Issue:32 Volume:9 Page:34725-34734
ISSN:2470-1343
Container-title:ACS Omega
language:en
Short-container-title:ACS Omega

Author:

Chen Hao¹^ORCID,Zhang Haifei¹,Yang Yuwei¹,Zhang Qiong¹

Affiliation:

1. School of Information Engineering, Nantong Institute of Technology, Nantong 226002, China

Funder

Natural Science Foundation of Nantong Municipality

Ministry of Education Industry-Education Cooperation Collaborative Education Project

Natural Science Foundation of University in Jiangsu Province

Jiangsu Provincial Department of Education

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acsomega.4c03747

Reference33 articles.

1. Survey on data-driven industrial process monitoring and diagnosis

2. A review of data-driven fault detection and diagnosis methods: applications in chemical process systems

3. Attention-Based Interval Aided Networks for Data Modeling of Heterogeneous Sampling Sequences With Missing Values in Process Industry

4. Integrating feature optimization using a dynamic convolutional neural network for chemical process supervised fault classification

5. Yuan, X.; Wang, Y.; Wang, C.; Ye, L.; Wang, K.; Wang, Y.; Yang, C.; Gui, W.; Shen, F. Variable correlation analysis-based convolutional neural network for far topological feature extraction and industrial predictive modeling IEEE Trans. Instrum. Meas. 2024; Vol. 73 10.1109/TIM.2024.3373085.

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