ProbSparse Attention-Based Fault Diagnosis for Industrial Robots Under Different Working Conditions-Reference-Cited by-同舟云学术

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ProbSparse Attention-Based Fault Diagnosis for Industrial Robots Under Different Working Conditions

Published:2024 Issue: Volume:73 Page:1-12
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Wang Yimo¹^ORCID,He Yiming²^ORCID,Kang Bin³^ORCID,Liu Jian¹^ORCID,Sun Changyin¹^ORCID

Affiliation:

1. School of Automation, Southeast University, Nanjing, China

2. State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, China

3. School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing, China

Funder

National Natural Science Foundation of China

Key Project of Natural Science Foundation of Jiangsu Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/19/10367905/10465665.pdf?arnumber=10465665

Reference38 articles.

1. Deep Model Based Domain Adaptation for Fault Diagnosis

2. Deep Semi-supervised Domain Generalization Network for Rotary Machinery Fault Diagnosis under Variable Speed

3. In-situ fault detection for the spindle motor of CNC machines via multi-stage residual fusion convolution neural networks

4. Intelligent Fault Diagnosis With Deep Adversarial Domain Adaptation

5. Domain generalization in rotating machinery fault diagnostics using deep neural networks

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