Fully Simulated-Data-Driven Transfer-Learning Method for Rolling-Bearing-Fault Diagnosis

Author:

Ai Ting1ORCID,Liu Zhiliang1ORCID,Zhang Jiyang2ORCID,Liu Honghao1ORCID,Jin Yaqiang3ORCID,Zuo Mingjian4ORCID

Affiliation:

1. School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China

2. School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, China

3. Center for Structural Acoustics and Machine Fault Diagnosis, Qingdao University of Technology, Qingdao, China

4. Qingdao International Academician Park Research Institute, Qingdao, China

Funder

National Key Research and Development Program of China

Sichuan Province Key Research and Development Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Cited by 10 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A systematic literature review of deep learning for vibration-based fault diagnosis of critical rotating machinery: Limitations and challenges;Journal of Sound and Vibration;2024-11

2. Cross-device fault diagnosis of rolling bearings using domain generalization and dynamic model;Journal of Vibration and Control;2024-06-01

3. Intelligent Joint Actuator Fault Diagnosis for Heavy-Duty Industrial Robots;IEEE Sensors Journal;2024-05-01

4. Hidden Markov Model for Hard Disk Drive Failure Detection;2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI);2024-03-14

5. Convolutional-Attention Hybrid Deep-Learning Model With Transfer Learning for Quality Inspection of DC Motors;IEEE Transactions on Instrumentation and Measurement;2024

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