Physics-Informed Machine Learning for Intelligent Predictive Maintenance in Smart Manufacturing Systems

Predictive maintenance (PdM) is critical for ensuring operational reliability and minimizing downtime in smart manufacturing systems. Traditional data-driven approaches often fail to capture underlying physical phenomena governing equipment degradation. This paper presents a novel Physics-Informed Machine Learning (PIML) framework that integrates domain knowledge from thermodynamics, vibration mechanics, and material science with advanced machine learning algorithms. We develop a hybrid model combining physics-based degradation equations with deep neural networks and ensemble methods to predict Remaining Useful Life (RUL) of industrial equipment. The proposed framework is validated using the NASA C-MAPSS turbofan engine degradation dataset and real-world bearing vibration data from manufacturing facilities. Experimental results demonstrate that PIML achieves 23.4% improvement in prediction accuracy compared to purely data-driven approaches, with Mean Absolute Error (MAE) reduced from 18.7 hours to 14.3 hours. The framework also exhibits superior generalization capability across different operating conditions and equipment types. Our contributions include: (1) a novel physics-constraint integration mechanism, (2) an adaptive feature engineering pipeline incorporating thermodynamic principles, and (3) a comprehensive evaluation on industrial datasets. This work advances the state-of-the-art in intelligent maintenance systems for Industry 4.0 applications.

Hali tarjima qilinmagan