Digital Twins for Detecting Anomalous Sensor behavior in Process Industries

Digital twin technology has emerged as a transformative tool in modern process industries, enabling realtime monitoring, optimization, and predictive analytics through virtual representations of physical systems. As industrial environments face increasing exposure to cyber-physical threats, the integrity of sensor data has become critical for ensuring safe and reliable operations. Anomalous sensor behaviors—whether caused by hardware faults, calibration drift, environmental interference, or intentional cyber manipulation—pose substantial risks to operational continuity and safety. Traditional anomaly detection techniques often rely on statistical thresholds or machine learning models that may fail to capture complex, multivariate deviations in highly dynamic industrial processes. This article explores how digital twins can serve as advanced detection mechanisms by continuously comparing real-time sensor data against physics-based simulations and production models. By integrating computational fluid dynamics (CFD), thermodynamic modeling, vibration profile simulation, and process control logic, digital twins enable early identification of inconsistencies between expected and observed behavior. This study develops a structured framework that incorporates model-based residual analysis, adaptive thresholds, time-series evaluation, and cyber-aware diagnostic rules. The findings demonstrate that digital twins can detect subtle anomalies, differentiate between physical degradation and cyber-induced manipulation, and enhance predictive maintenance accuracy. The proposed approach provides a robust foundation for improving data integrity assurance within complex industrial environments.