Physical Graph-Based Spatiotemporal Fusion Approach for Process Fault Diagnosis-Reference-Cited by-同舟云学术

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Physical Graph-Based Spatiotemporal Fusion Approach for Process Fault Diagnosis

Published:2024-02-16 Issue:8 Volume:9 Page:9486-9502
ISSN:2470-1343
Container-title:ACS Omega
language:en
Short-container-title:ACS Omega

Author:

Zhang Fengzhen¹,Jin Qibing¹,Li Dazi¹^ORCID,Zhang Yang²^ORCID,Zhu Qian³

Affiliation:

1. College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China

2. College of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, Beijing 100029, China

3. Jiangsu Academy of Chemical Inherent Safety, Jiangsu 210009, China

Funder

National Natural Science Foundation of China

Jiangsu Academy of Chemical Inherent Safety

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acsomega.3c09122

Reference44 articles.

1. A review of process fault detection and diagnosis

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

3. A review on fault detection and diagnosis techniques: basics and beyond

4. Data-Driven Fault Diagnosis for Traction Systems in High-Speed Trains: A Survey, Challenges, and Perspectives

5. Bayesian Networks in Fault Diagnosis

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. Graph Convolutional Network Based on CQT Spectrogram for Bearing Fault Diagnosis;Machines;2024-03-07

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