Joint Domain Adaptation-Based Lightweight Approach for Cross-Domain Diagnosis Compatible With Different Devices and Multimodal Sensing-Reference-Cited by-同舟云学术

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Joint Domain Adaptation-Based Lightweight Approach for Cross-Domain Diagnosis Compatible With Different Devices and Multimodal Sensing

Published:2024-09-01 Issue:17 Volume:24 Page:28373-28382
ISSN:1530-437X
Container-title:IEEE Sensors Journal
language:
Short-container-title:IEEE Sensors J.

Author:

Li Xuan¹^ORCID,Chen Qitong¹^ORCID,Chen Liang¹^ORCID,Shen Changqing¹^ORCID

Affiliation:

1. School of Mechanical and Electric Engineering, Soochow University, Suzhou, China

Funder

National Innovation and Development Project of Industrial Internet

National Natural Science Foundation of China

Suzhou Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/7361/10660640/10608057.pdf?arnumber=10608057

Reference32 articles.

1. Rotary Machinery Fault Diagnosis Based on Split Attention Mechanism and Graph Convolutional Domain Adaptive Adversarial Network

2. Rotating Machinery Fault Diagnosis Based on Typical Resonance Demodulation Methods: A Review

3. A rotating machinery fault diagnosis method based on local mean decomposition

4. Investigation of ball screw preload variation based on dynamic modeling of a preload adjustable feed-drive system and spectrum analysis of ball-nuts sensed vibration signals

5. Vibration Signals Analysis by Explainable Artificial Intelligence (XAI) Approach: Application on Bearing Faults Diagnosis

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