Adaptive Digital Twin Framework for Real-Time Asset Monitoring and Predictive Maintenance

Abstract

This study suggests a novel adaptive digital twin system, a synthesis between real-time IoT telemetry, physics-ML hybrid modeling, and closed-loop control to allow systematic asset health assessment and scalable predictive maintenance. The architecture is multi-layered: data acquisition and edge filtering; a streaming analytics layer with anomaly detection and remaining useful life (RUL) estimation; an adaptive model layer with self-calibration, online learning and parameter estimates; and a decision orchestration layer giving prescriptive maintenance instructions and scheduling updates back to the physical asset. Bidirectional data flows between models and automated model management is used to reduce model-plant mismatch in nonstationary environments, i.e., wear, drift, and changing operating regimes. We make recalibration triggers of model recalibration, between physics residuals and map embeddings define health, and uncertainty-aware schedules improve timing in maintenance and spare parts logistics and production constraints. The architecture achieves heterogeneous assets and old systems through common interfaces, hence resolving interoperability gaps found in recent survey of experts. The representative industrial machinery assessment shows a better early-fault detectability, less falseness and longer maintenance times than when using the static models, as well as not compromising explain ability via physics-constrained predictors. The findings point to a road to stable, self improving digital twins that bring quantifiable uptime, cost and safety ascent in real time functioning.