Concurrent Monitoring Strategy for Static and Dynamic Deviations Based on Selective Ensemble Learning Using Slow Feature Analysis-Reference-Cited by-同舟云学术

Concurrent Monitoring Strategy for Static and Dynamic Deviations Based on Selective Ensemble Learning Using Slow Feature Analysis

Published:2020-02-12 Issue:10 Volume:59 Page:4620-4635
ISSN:0888-5885
Container-title:Industrial & Engineering Chemistry Research
language:en
Short-container-title:Ind. Eng. Chem. Res.

Author:

Hong Huifen¹,Jiang Chao¹²^ORCID,Peng Xin¹³,Zhong Weimin¹⁴^ORCID

Affiliation:

1. Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China

2. Department of Chemical and Materials Engineering, University of Alberta, Edmonton, Alberta T6G 2V4, Canada

3. The Institute for Automatic Control and Complex Systems, University of Duisburg-Essen, Duisburg 47057, Germany

4. Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai 200092, China

Funder

State Administration of Foreign Experts Affairs

Ministry of Education of the People's Republic of China

Science and Technology Commission of Shanghai Municipality

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.9b05547

Reference38 articles.

1. Data-driven Detection and Diagnosis of Incipient Faults in Electrical Drives of High-Speed Trains

2. A Review of Fault Detection and Diagnosis for the Traction System in High-Speed Trains

3. Data-Based Techniques Focused on Modern Industry: An Overview

4. Statistical process monitoring of a multiphase flow facility

5. Distributed plant-wide process monitoring based on PCA with minimal redundancy maximal relevance

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