Research on bearing fault diagnosis based on machine learning and SHAP interpretability analysis

Bearing faults are the most common type of failures in rotating machinery, accounting for approximately 30%-40% of total failures and posing serious threats to industrial production safety and economic efficiency. Traditional diagnostic methods based on physical models and empirical thresholds suffer from poor adaptability and low accuracy, while existing machine learning methods lack interpretability in their decision-making processes, limiting their application and promotion in industrial settings. This research proposes an integrated bearing fault diagnosis method combining multiple machine learning algorithms with SHAP interpretability analysis. A comprehensive experimental platform was constructed, including motors, couplings, loading systems and high-precision data acquisition systems, collecting vibration signals from normal and faulty bearings at a sampling frequency of 25.6 kHz. Raw signals were preprocessed using sliding window techniques and 15 key features covering time-domain, frequency-domain, and statistical characteristics were extracted, forming a comprehensive multi-domain fusion feature system. Ten representative machine learning algorithms (including LogReg, SVM, KNN, DT, RF, AdaBoost, GBoost, XGBoost, LGBM and NB) were systematically compared and analyzed, with comprehensive evaluation using multi-dimensional metrics including accuracy, precision, recall, F1-score, ROC AUC and log loss. Experimental results show that the XGBoost model performed optimally, achieving 91.0% accuracy, 91.9% precision, 98.9% recall, 95.3% F1-score and 62.7% ROC AUC. Multiple models achieved 100% recall, effectively avoiding fault omission. SHAP technology was systematically applied to interpretability analysis in bearing fault diagnosis, revealing that spectral_entropy, rms and impulse_factor are the most important features influencing diagnostic decisions, with their importance ranking highly consistent with the physical mechanisms of bearing faults. SHAP analysis not only provides transparent interpretation of model decisions but also offers scientific basis for feature engineering optimization and industrial applications. This research provides a complete, efficient and interpretable intelligent diagnostic solution for bearing fault diagnosis, with significant implications for advancing predictive maintenance technology development.