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Highly Accurate Interpretable Bearing Fault Diagnosis Based on SHAP-RFE with Bayesian Optimization Support Vector Machines

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The method achieves over 98.9% accuracy in diagnosing bearing faults while maintaining strong interpretability.
SHAP-RFE simplifies the feature matrix, enhancing both model performance and generalization capabilities.
Bayesian optimization fine-tunes the support vector machine's hyper-parameters, leading to better classification accuracy.
Experimental validation with bearing vibration datasets demonstrates the effectiveness and robustness of the proposed method.

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This method improves classification accuracy and feature selection in bearing fault diagnosis, indicating better interpretability.

Wang et al. (2025) studied this question.

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