Autocorrelation Feature Analysis for Dynamic Process Monitoring of Thermal Power Plants-Reference-Cited by-同舟云学术

Autocorrelation Feature Analysis for Dynamic Process Monitoring of Thermal Power Plants

Published:2022 Issue: Volume: Page:1-13
ISSN:2168-2267
Container-title:IEEE Transactions on Cybernetics
language:
Short-container-title:IEEE Trans. Cybern.

Author:

Ma Xin¹^ORCID,Wu Dehao²,Gao Shaoxu¹,Hou Tongze¹,Wang Youqing³^ORCID

Affiliation:

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

2. School of Automation, Central South University, Changsha, China

3. College of Information Science and Technology and the State Key Laboratory of Chemical Resource Engineering, Beijing University of Chemical Technology, Beijing, China

Funder

National Science Fund for Distinguished Young Scholars of China

Fundamental Research Funds for the Central Universities

National Natural Science Foundation of China

High Performance Computing Platform, College of Information Science and Technology, Beijing University of Chemical Technology

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Human-Computer Interaction,Information Systems,Control and Systems Engineering,Software

Link

http://xplorestaging.ieee.org/ielx7/6221036/6352949/10002852.pdf?arnumber=10002852

Cited by 11 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. Fault Diagnosis Method of the USV Based on Improved Res2Net Multiscale Feature Fusion;IEEE Sensors Journal;2024-08-01

2. Overview and prospect of abnormal monitoring methods for non-stationary processes;SCIENTIA SINICA Informationis;2024-08-01

3. Enhanced process monitoring for industrial coking furnace using a dual‐channel pooling and homologous bilinear model‐based convolutional neural network;The Canadian Journal of Chemical Engineering;2024-03-08

4. Knowledge-Enhanced Distributed Graph Autoencoder for Multiunit Industrial Plant-Wide Process Monitoring;IEEE Transactions on Industrial Informatics;2024-02

5. Twofold Weighted-Based Statistical Feature KECA for Nonlinear Industrial Process Fault Diagnosis;IEEE Transactions on Automation Science and Engineering;2024