Differential-Augmented Current Feature Learning Network With Multi-Information Interaction for Fault Diagnosis in Electromechanical Drive System-Reference-Cited by-同舟云学术

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Differential-Augmented Current Feature Learning Network With Multi-Information Interaction for Fault Diagnosis in Electromechanical Drive System

Published:2023-07-15 Issue:14 Volume:23 Page:15942-15951
ISSN:1530-437X
Container-title:IEEE Sensors Journal
language:
Short-container-title:IEEE Sensors J.

Author:

He Qun¹,Zhao Ruchun¹^ORCID,Jiang Guoqian¹^ORCID,Xie Ping¹^ORCID

Affiliation:

1. School of Electrical Engineering, Yanshan University, Qinhuangdao, China

Funder

National Natural Science Foundation of China

Hebei Province Science and Technology Support Program

Natural Science Foundation of Hebei Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/7361/10183770/10146279.pdf?arnumber=10146279

Reference33 articles.

1. Detection of Bearing Faults in Mechanical Systems Using Stator Current Monitoring

2. EEMD-based wind turbine bearing failure detection using the generator stator current homopolar component

3. A Novel Empirical Mode Decomposition With Support Vector Regression for Wind Speed Forecasting

4. Time-Varying and Multiresolution Envelope Analysis and Discriminative Feature Analysis for Bearing Fault Diagnosis

5. Task-Sequencing Meta Learning for Intelligent Few-Shot Fault Diagnosis with Limited Data

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