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The aim is to develop a predictive maintenance framework using AI for diagnosing faults in rolling-element bearings through vibration analysis.

April 19, 2026Open Access

An AI-Driven Predictive Maintenance Framework for Rolling-Element Bearing Fault Diagnosis Using Vibration Signal Analysis

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The aim is to develop a predictive maintenance framework using AI for diagnosing faults in rolling-element bearings through vibration analysis.
Utilized minimal signal pre-processing and time-domain statistical feature extraction.
Employed a Random Forest classifier for fault classification.
Conducted experimental evaluations using the CWRU dataset and McNemar's test for performance validation.
Integrated the model into a web-based platform for real-time fault prediction.
Achieved an overall classification accuracy of 98.7% and a macro-averaged F1-score of 0.990.
Demonstrated 96.2% accuracy under cross-load generalisation conditions.
Validated the significance of performance improvements using McNemar's test.

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AI-driven framework achieves high classification accuracy in bearing fault diagnosis, suggesting practical industrial applications.

Gajendran R (2026) studied this question.

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