A Hybrid Intelligent Framework for Uncertainty-Aware Condition Monitoring of Industrial Systems

ArXi:2604.09932v1 Announce Type: cross Hybrid approaches that combine data-driven learning with physics-based insight have shown promise for improving the reliability of industrial condition monitoring. This work develops a hybrid condition monitoring framework that integrates primary sensor measurements, lagged temporal features, and physics-informed residuals derived from nominal surrogate models. Two hybrid integration strategies are examined. The first is a feature-level fusion approach that augments the input space with residual and temporal information.