Unsupervised Fault Detection With Deep One-Class Classification and Manifold Distribution Alignment-Reference-Cited by-同舟云学术

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Unsupervised Fault Detection With Deep One-Class Classification and Manifold Distribution Alignment

Published:2024-02 Issue:2 Volume:20 Page:1313-1323
ISSN:1551-3203
Container-title:IEEE Transactions on Industrial Informatics
language:
Short-container-title:IEEE Trans. Ind. Inf.

Author:

Ding Yifei¹^ORCID,Jia Minping¹^ORCID,Cao Yudong¹^ORCID,Yan Xiaoan²^ORCID,Zhao Xiaoli³^ORCID,Lee Chi-Guhn⁴^ORCID

Affiliation:

1. School of Mechanical Engineering, Southeast University, Nanjing, China

2. School of Mechatronics Engineering, Nanjing Forestry University, Nanjing, China

3. School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing, China

4. Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada

Funder

National Natural Science Foundation of China

Postgraduate Research & Practice Innovation Program of Jiangsu Province

China Scholarship Council

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/ielx7/9424/10411967/10123967.pdf?arnumber=10123967

Reference40 articles.

1. Novel motor fault detection scheme based on one-class tensor hyperdisk

2. Dual-Threshold Attention-Guided GAN and Limited Infrared Thermal Images for Rotating Machinery Fault Diagnosis Under Speed Fluctuation

3. End-to-end unsupervised fault detection using a flow-based model

4. Online detection for bearing incipient fault based on deep transfer learning

5. Self-supervised pretraining via contrast learning for intelligent incipient fault detection of bearings

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1. Approximate and Memorize (A&M) : Settling opposing views in replay-based continuous unsupervised domain adaptation;Knowledge-Based Systems;2024-06

2. NorCLR: A Normality-Aggregated Contrastive Learning Framework for Mechanical Anomaly Detection;2024 IEEE International Instrumentation and Measurement Technology Conference (I2MTC);2024-05-20

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