September 5, 2025Journal of University of Babylon for Engineering SciencesOpen Access

A Comprehensive Review of Machine Learning Algorithms for Fault ‎Diagnosis and Prediction in Rotating Machinery

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Machine learning algorithms significantly improve fault diagnosis in rotating machinery, enhancing operational efficiency.
Techniques like convolutional neural networks and support vector machines classify fault patterns from sensor data effectively.
Dimensionality reduction methods simplify complex data structures, aiding in the detection of crucial defect signals.
Advanced algorithms and signal processing techniques improve predictive maintenance strategies in modern manufacturing.

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Implication

This review showcases various machine learning techniques and their effectiveness in fault diagnosis, suggesting improvements in predictive maintenance.

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Abdullah et al. (2025) studied this question.

synapsesocial.com/papers/68c238d2b210217d6477934f https://doi.org/10.29196/jubes.v33i4.5891

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Implication

This review showcases various machine learning techniques and their effectiveness in fault diagnosis, suggesting improvements in predictive maintenance.

A Comprehensive Review of Machine Learning Algorithms for Fault ‎Diagnosis and Prediction in Rotating Machinery

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A Comprehensive Review of Machine Learning Algorithms for Fault ‎Diagnosis and Prediction in Rotating Machinery

Key Points

Discussion

Implication

Cite This Study

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