An Intelligent Fault Diagnosis Scheme for Rotating Machinery Based on Supervised Domain Adaptation With Manifold Embedding-Reference-Cited by-同舟云学术

An Intelligent Fault Diagnosis Scheme for Rotating Machinery Based on Supervised Domain Adaptation With Manifold Embedding

Published:2023-01-01 Issue:1 Volume:10 Page:953-972
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Yu Xiao¹^ORCID,Dong Fei²^ORCID,Xia Bing¹,Yang Shuxin¹,Ding Enjie¹^ORCID,Yu Wanli³^ORCID

Affiliation:

1. IoT Perception Mine Research Center and the School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China

2. School of Internet, Anhui University, Hefei, China

3. Institute of Electrodynamics and Microelectronics, University of Bremen, Bremen, Germany

Funder

Youth Science and Technology Fund of China University of Mining and Technology

Jiangsu Postdoctoral Research Support Program

National Key Research and Development Program of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Computer Science Applications,Hardware and Architecture,Information Systems,Signal Processing

Link

http://xplorestaging.ieee.org/ielx7/6488907/9997152/09951150.pdf?arnumber=9951150

Reference55 articles.

1. Transfer fault diagnosis of bearing installed in different machines using enhanced deep auto-encoder

2. A framework for semi-supervised metric transfer learning on manifolds

3. Deep transfer multi-wavelet auto-encoder for intelligent fault diagnosis of gearbox with few target training samples

4. Feature Space Transformation for Fault Diagnosis of Rotating Machinery under Different Working Conditions

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