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Multi-domain feature-based early detection of bearing faults using MLP classifier on NASA IMS dataset

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A classification accuracy of 86.5% was achieved using an MLP model on the NASA IMS Bearing dataset.
This approach outperformed traditional classifiers such as Support Vector Machine and Random Forest, especially in data-scarce environments.
Multi-domain statistical feature extraction was utilized to enhance bearing fault detection precision and effectiveness.
The method is applicable for deployment on resource-constrained embedded systems, highlighting its practical utility.

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Proposed method improves bearing fault detection accuracy in industrial settings, suggesting benefits for predictive maintenance.

Van et al. (2025) studied this question.

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