Abstract:

The transition to Industry 4.0 has continued to evolutionise maintenance strategies, shifting from reactive approaches toward new, data-driven predictive maintenance systems. This study evaluates three machine learning approaches—Random Forest, XGBoost, and Long Short-Term Memory networks—for equipment failure prediction using live sensor data from rotating machinery. The study is based on feature engineering from multi-sensor time-series data and time-dependent validation protocols. The research demonstrates that mixed methods achieve superior performance in binary failure classification (Random Forest with 94.2% accuracy, XGBoost - 93.7%), while Long Short-Term Memory networks perform particularly well in remaining useful life prediction with RMSE values 23% lower than traditional approaches. The analysis reveals that model selection should be guided by specific maintenance objectives: immediate failure detection favors mixed methods, whereas lifecycle planning benefits from deep learning architectures. The study identifies key implementation barriers including data quality, computational requirements, and integration with Manufacturing Execution Systems. Comparative analysis demonstrates significant economic viability with 15-month payback periods and >80% annual ROI in high-downtime environments.