Disentanglement Learning With Adaptive Centroid Alignment for Multiple Target Domains Fault Diagnosis-Reference-Cited by-同舟云学术

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Disentanglement Learning With Adaptive Centroid Alignment for Multiple Target Domains Fault Diagnosis

Published:2024-09 Issue:9 Volume:20 Page:10779-10790
ISSN:1551-3203
Container-title:IEEE Transactions on Industrial Informatics
language:
Short-container-title:IEEE Trans. Ind. Inf.

Author:

Gao Yu¹^ORCID,Zheng Xutao¹^ORCID,Li Jinxing¹^ORCID,Zong Lijun²^ORCID,Yin Hongpeng³^ORCID,Li Huafeng⁴^ORCID,Lu Guangming¹^ORCID

Affiliation:

1. Department of Computer Science, Harbin Institute of Technology at Shenzhen, Shenzhen, China

2. Northwestern Polytechnical University, Xi'an, China

3. Key Laboratory of Complex System Safety and Control, Ministry of Education, Chongqing University, Chongqing, China

4. Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China

Funder

National Natural Science Foundation of China

Shenzhen Key Technical

Guangdong International Science and Technology Cooperation

Shenzhen Fundamental Research and Discipline Layout project

Key Laboratory of Industrial Equipment Quality Big Data

Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies

Shenzhen Colleges and Universities Stable Support Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/9424/10666802/10531759.pdf?arnumber=10531759

Reference43 articles.

1. Applications of machine learning to machine fault diagnosis: A review and roadmap

2. Fault detection of wheel in wheel/rail system using kurtosis beamforming method

3. Fault Feature Extractor based on Bootstrap Your Own Latent and Data Augmentation Algorithm for Unlabeled Vibration Signals

4. Denoising Fault-Aware Wavelet Network: A Signal Processing Informed Neural Network for Fault Diagnosis

5. A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method

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