MNHP-GAE: A Novel Manipulator Intelligent Health State Diagnosis Method in Highly Imbalanced Scenarios-Reference-Cited by-同舟云学术

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MNHP-GAE: A Novel Manipulator Intelligent Health State Diagnosis Method in Highly Imbalanced Scenarios

Published:2024-07-01 Issue:13 Volume:11 Page:24073-24082
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Zhao Bo¹^ORCID,Wu Qiqiang²^ORCID,Zhao Ke³^ORCID,Mo Zhenling¹^ORCID,Zhang Zijun¹^ORCID,Zhang Xianmin²^ORCID

Affiliation:

1. Hong Kong Institute for Data Science, City University of Hong Kong, Hong Kong, SAR, China

2. School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, China

3. Key Laboratory of Road Construction Technology and Equipment Ministry of Education, Chang’an University, Xi’an, China

Funder

National Natural Science Foundation of China

Shenzhen– Hong Kong–Macau Science and Technology Category C Project

Changsha Technology Project

InnoHK Initiative, The Government of the HKSAR

Laboratory for AI-Powered Financial Technologies

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6488907/10570342/10500838.pdf?arnumber=10500838

Reference36 articles.

1. Deep Learning Domain Adaptation for Electro-Mechanical Actuator Fault Diagnosis Under Variable Driving Waveforms

2. Semi-supervised adversarial discriminative learning approach for intelligent fault diagnosis of wind turbine

3. Class-Aware Adversarial Multiwavelet Convolutional Neural Network for Cross-Domain Fault Diagnosis

4. Fusing convolutional generative adversarial encoders for 3D printer fault detection with only normal condition signals

5. Unified motion reliability analysis and comparison study of planar parallel manipulators with interval joint clearance variables

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1. A composite testing method of homologous magnetic flux leakage and motion-induced eddy current for inner and outer defects;Measurement;2025-01

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