Balancing the Imbalance: Advanced Anomaly Detection for Predictive Maintenance

Predictive maintenance is a crucial application in industrial systems, ensuring timely interventions to prevent failures and reduce downtime. However, the highly imbalanced nature of failure datasets poses a significant challenge to traditional machine learning models. This paper proposes an advanced anomaly detection framework utilizing synthetic data generation, oversampling techniques (SMOTE, ADASYN), and cost-sensitive learning to enhance failure detection accuracy. The system integrates Random Forest and XGBoost classifiers, optimized through hyperparameter tuning, to improve predictive maintenance efficiency. The proposed method demonstrates improved recall and precision, reducing false negatives while maintaining robustness in detecting rare failure events.