Enhancing data anomaly prediction and real-time physical problem detection with Digital Twins and Cognitive Super Digital Twins

The increasing reliance on Internet of Things (IoT) systems has highlighted the need for effective strategies to detect data anomalies and address physical malfunctions in real time. This paper proposes a Cognitive Super Digital Twin (CSDT) to detect data anomalies and flag physical problems in IoT systems. The framework augments a standard DT with a synthetic data layer that balances rare events and improves model learning. We validate the method on an environmental-sensing and robot actuation case study, showing higher recall and F1 when training with augmented data, and a practical DT that detects incomplete robot motions. The approach improves resilience while keeping costs low by prioritizing digital experiments before physical changes. • Innovative Framework: The Cognitive Super Digital Twin (CSDT) extends the traditional digital twin concept by integrating cognitive capabilities for enhanced data anomaly prediction and real-time physical problem detection. • Resilience Enhancement: Our approach emphasizes proactive monitoring and predictive analysis to improve system resilience in complex IoT environments. • Case Study Validation: We demonstrate the effectiveness of the CSDT using an IoT-based environmental monitoring system integrated with a robotic platform, showcasing its practical applicability and accuracy. • Methodological Contribution: The paper presents a robust combination of model-driven engineering, machine learning algorithms, and digital twin technology, contributing to the field’s body of knowledge.