Multi-scale Transformer-CNN domain adaptation network for complex processes fault diagnosis-Reference-Cited by-同舟云学术

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Multi-scale Transformer-CNN domain adaptation network for complex processes fault diagnosis

Published:2023-10 Issue: Volume:130 Page:103069
ISSN:0959-1524
Container-title:Journal of Process Control
language:en
Short-container-title:Journal of Process Control

Author:

Zhu Qun-Xiong,Qian Yu-Shi,Zhang Ning,He Yan-Lin,Xu Yuan

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Industrial and Manufacturing Engineering,Computer Science Applications,Modeling and Simulation,Control and Systems Engineering

Reference39 articles.

1. Performance supervised plant-wide process monitoring in industry 4.0: A roadmap;Jiang;IEEE Open J. Ind. Electron. Soc.,2020

2. From model, signal to knowledge: A data-driven perspective of fault detection and diagnosis;Dai;IEEE Trans. Ind. Inform.,2013

3. Deep learning enabled intelligent fault diagnosis: Overview and applications;Duan;J. Intell. Fuzzy Systems,2018

4. An optimized long short-term memory network based fault diagnosis model for chemical processes;Han;J. Process Control,2020

5. A review on the application of deep learning in system health management;Khan;Mech. Syst. Signal Process.,2018

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2. Cross-domain fault diagnosis for multimode green ammonia synthesis process based on DA-CycleGAN;Process Safety and Environmental Protection;2024-11

3. Unsupervised transfer learning for fault diagnosis across similar chemical processes;Process Safety and Environmental Protection;2024-10

4. Intelligent fault diagnosis of rolling bearing based on an active federated local subdomain adaptation method;Advanced Engineering Informatics;2024-10

5. Category-tree-guided hierarchical knowledge transfer framework for zero-shot fault diagnosis;Journal of Process Control;2024-09

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