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

<p dir="ltr">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...

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Bibliographic Details
Main Author:	Durjay Saha (21633095) (author)
Other Authors:	Md. Emdadul Hoque (20080485) (author), Muhammad E. H. Chowdhury (14150526) (author)
Published:	2024
Subjects:	Engineering Mechanical engineering Information and computing sciences Machine learning Bearing fault bearing fault classification bearing fault classification under variable working conditions machine condition monitoring random forest transfer learning VGG16 Adaptation models Fault diagnosis Convolutional neural networks Feature extraction Employee welfare Transfer learning Condition monitoring Random forests
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Enhancing Bearing Fault Diagnosis Using Transfer Learning and Random Forest Classification: A Comparative Study on Variable Working Conditions

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