An Adversarial Multisource Data Subdomain Adaptation Model: A Promising Tool for Fault Diagnosis of Induction Motor Under Cross-Operating Conditions-Reference-Cited by-同舟云学术

An Adversarial Multisource Data Subdomain Adaptation Model: A Promising Tool for Fault Diagnosis of Induction Motor Under Cross-Operating Conditions

Published:2023 Issue: Volume:72 Page:1-14
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Shi Jiancong¹^ORCID,Wang Xinglong¹^ORCID,Lu Siliang²^ORCID,Zheng Jinde³^ORCID,Dong Hui¹^ORCID,Zhang Jun¹^ORCID

Affiliation:

1. School of Mechanical Engineering and Automation, Fuzhou University, Fujian, Fuzhou, China

2. National Engineering Laboratory of Energy-Saving Motor and Control Technology, College of Electrical Engineering and Automation, Anhui University, Hefei, China

3. School of Mechanical Engineering, Anhui University of Technology, Anhui, Maanshan, China

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/19/10012124/10143342.pdf?arnumber=10143342

Reference44 articles.

1. Knowledge mapping-based adversarial domain adaptation: A novel fault diagnosis method with high generalizability under variable working conditions

2. Online Fault Diagnosis Method Based on Transfer Convolutional Neural Networks

3. Fault detection and diagnosis in electric motors using 1d convolutional neural networks with multi-channel vibration signals

4. Machine Fault Diagnosis Method Using Lightweight 1-D Separable Convolution and WSNs With Sensor Computing

5. Domain-Adversarial Training of Neural Networks

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