Fault Diagnosis of Unseen Modes in Chemical Processes Based on Labeling and Class Progressive Adversarial Learning-Reference-Cited by-同舟云学术

Fault Diagnosis of Unseen Modes in Chemical Processes Based on Labeling and Class Progressive Adversarial Learning

Published:2023 Issue: Volume:72 Page:1-12
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Xiao Yutang¹^ORCID,Shi Hongbo¹^ORCID,Wang Boyu²^ORCID,Tao Yang¹^ORCID,Tan Shuai¹^ORCID,Song Bing¹^ORCID

Affiliation:

1. Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China

2. Department of Computer Science and the Brain Mind Institute, University of Western Ontario, London, ON, Canada

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Shanghai Rising-Star Program

National Natural Science Foundation of Shanghai

Shanghai Chenguang Project

Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grants Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/19/10012124/09989345.pdf?arnumber=9989345

Reference34 articles.

1. Nonlinear dynamic process monitoring based on dynamic kernel PCA

2. Statistical process monitoring of a multiphase flow facility

3. A plant-wide industrial process control problem

4. A Supervised Adaptive Resampling Monitoring Method for Quality Indicator in Time-Varying Process

5. Supervision, fault-detection and fault-diagnosis methods — An introduction

Cited by 1 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. VIT-GADG: A Generative Domain-Generalized Framework for Chillers Fault Diagnosis Under Unseen Working Conditions;IEEE Transactions on Instrumentation and Measurement;2023