Artificial Intelligence-based Fault Detection and Diagnosis: Towards Application in a Chemical Process-Reference-Cited by-同舟云学术

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Artificial Intelligence-based Fault Detection and Diagnosis: Towards Application in a Chemical Process

Published:2023-09-22 Issue: Volume: Page:
ISSN:
Container-title:2023 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS)
language:
Short-container-title:

Author:

Liu Yiqi¹,Li Zhi¹,Chen Hongtian²

Affiliation:

1. The School of Automation Science & Engineering South China University of Technology,Key Laboratory of Autonomous Systems and Networked Control, Ministry of Education,Guangzhou,China,510640

2. Shanghai Jiao Tong University,Department of Automation,Shanghai,China,200240

Funder

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10295546/10295577/10295864.pdf?arnumber=10295864

Reference17 articles.

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

2. Transfer learning-motivated intelligent fault diagnosis designs: A survey, insights, and perspectives;chen,2022

3. Fast anomaly detection using Boxplot rule for multivariate data in cooperative wideband cognitive radio in the presence of jammer

4. An explainable artificial intelligence approach for unsupervised fault detection and diagnosis in rotating machinery

5. Interpretable Anomaly Detection with DIFFI: Depth-based feature importance of Isolation Forest

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. Explainable AI in Manufacturing and Industrial Cyber–Physical Systems: A Survey;Electronics;2024-09-03

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