Enhancing Bearing Fault Diagnosis Using Transfer Learning and Random Forest Classification: A Comparative Study on Variable Working Conditions
Author:
Affiliation:
1. Department of Mechanical Engineering, Rajshahi University of Engineering and Technology, Rajshahi, Bangladesh
2. Department of Electrical Engineering, Qatar University, Doha, Qatar
Funder
University Grants Commission, Bangladesh
Rajshahi University of Engineering and Technology, Bangladesh
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/6287639/10380310/10374127.pdf?arnumber=10374127
Reference39 articles.
1. Testing and Analysis Fault of Induction Motor for Case Study Misalignment Installation Using Current Signal with Energy Coefficient
2. Rotating machinery prognostics: State of the art, challenges and opportunities
3. A Novel Feature Enhancement Method Based on Improved Constraint Model of Online Dictionary Learning
4. Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study
5. Deep Learning Based Approach for Bearing Fault Diagnosis
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3. Improved Adversarial Transfer Network for Bearing Fault Diagnosis under Variable Working Conditions;Applied Sciences;2024-03-07
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