Benchmarking Machine Learning Algorithms for Bearing Fault Classification Using Vibration Data: A Deployment-Oriented Study

Benchmarking Machine Learning Algorithms for Bearing Fault Classification Using Vibration Data: A Deployment-Oriented Study

This study presents a comprehensive benchmarking of 33 machine learning (ML) algorithms for bearing fault classification using vibration data, with a focus on real-world deployment in condition monitoring systems. A total of 81,000 samples were collected from three case studies involving SKF7205, SK...

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Bibliographic Details
Main Authors: Prasanta Kumar Samal, R. Srinidhi, Pramod Kumar Malik, H. J. Manjunatha, Imran M. Jamadar
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Bearing fault diagnosis
ML algorithm
feature selection
principal component analysis (PCA)
predictive maintenance
industrial deployment
Online Access:https://ieeexplore.ieee.org/document/11045386/
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