Collaborative and Conditional Deep Adversarial Network for Intelligent Bearing Fault Diagnosis-Reference-Cited by-同舟云学术

Collaborative and Conditional Deep Adversarial Network for Intelligent Bearing Fault Diagnosis

Published:2022 Issue: Volume:71 Page:1-13
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Xia Yi¹^ORCID,Zhang Chengzhi²^ORCID,Ye Qiang³^ORCID,Lu Yixiang²^ORCID,Yang Runyu²^ORCID,Wu Yuhui²^ORCID

Affiliation:

1. Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Electrical Engineering and Automation, Anhui University, Hefei, China

2. School of Electrical Engineering and Automation, Anhui University, Hefei, China

3. School of Physical Education and Humanities, Nanjing Sport Institute, Nanjing, China

Funder

Anhui Provincial Natural Science Foundation

Ministry of Education of Humanities and Social Science Project of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/19/9717300/09906003.pdf?arnumber=9906003

Reference41 articles.

1. Conditional Adversarial Domain Adaptation With Discrimination Embedding for Locomotive Fault Diagnosis

2. Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study

3. Domain Adversarial Graph Convolutional Network for Fault Diagnosis Under Variable Working Conditions

4. A novel method for diagnosing bearing transfer faults based on a maximum mean discrepancies guided domain-adversarial mechanism

5. Deep Convolutional Transfer Learning Network: A New Method for Intelligent Fault Diagnosis of Machines With Unlabeled Data

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