Physical Model Informed Fault Detection and Diagnosis of Air Handling Units Based on Transformer Generative Adversarial Network-Reference-Cited by-同舟云学术

Physical Model Informed Fault Detection and Diagnosis of Air Handling Units Based on Transformer Generative Adversarial Network

Published:2023-02 Issue:2 Volume:19 Page:2192-2199
ISSN:1551-3203
Container-title:IEEE Transactions on Industrial Informatics
language:
Short-container-title:IEEE Trans. Ind. Inf.

Author:

Yan Ke¹^ORCID,Chen Xinke¹,Zhou Xiaokang²^ORCID,Yan Zheng³^ORCID,Ma Jianhua⁴

Affiliation:

1. Key Laboratory of Electromagnetic Wave Information Technology and Metrology of Zhejiang Province, College of Information Engineering, China Jiliang University, Hangzhou, China

2. Faculty of Data Science, Shiga University, Hikone, Japan

3. State Key Laboratory on Integrated Services Networks and the School of Cyber Engineering, Xidian University, Xi'an, China

4. Faculty of Computer and Information Sciences, Hosei University, Chiyoda-ku, Japan

Funder

Singapore MOE Tier 1 Fundings

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Information Systems,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/9424/9989328/09839583.pdf?arnumber=9839583

Reference27 articles.

1. Fault detection and diagnosis of large-scale HVAC systems in buildings using data-driven methods: A comprehensive review

2. Model-based fault-detection and diagnosis – status and applications

3. A Novel Challenge into Multimedia Cultural Heritage: An Integrated Approach to Support Cultural Information Enrichment

4. Fault detection and diagnosis with a novel source-aware autoencoder and deep residual neural network

5. Fault detection and diagnosis for chiller based on feature-recognition model and Kernel Discriminant Analysis

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