Enhancing Bearing Fault Diagnosis Using Transfer Learning and Random Forest Classification: A Comparative Study on Variable Working Conditions

Enhancing Bearing Fault Diagnosis Using Transfer Learning and Random Forest Classification: A Comparative Study on Variable Working Conditions

Rotating machines require bearings to operate smoothly. However, wear, misalignment, and poor lubrication can degrade bearings over time. Fault diagnosis models identify and classify bearing faults. A fault diagnosis model trained in a specific working condition may not perform well in different wor...

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Bibliográfalaš dieđut
Váldodahkkit: Durjay Saha, Md. Emdadul Hoque, Muhammad E. H. Chowdhury
Materiálatiipa: Artihkal
Giella:English
Almmustuhtton: IEEE 2024-01-01
Ráidu:IEEE Access
Fáttát:
Bearing fault
bearing fault classification
bearing fault classification under variable working conditions
machine condition monitoring
random forest
transfer learning
Liŋkkat:https://ieeexplore.ieee.org/document/10374127/

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https://ieeexplore.ieee.org/document/10374127/

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