An anomaly detection framework for digital twin driven cyber-physical systems

In recent years, the digital twin has been one of the active research areas in modern Cyber-Physical Systems (CPS). Both the digital twin and its physical counterpart, called a plant, are highly intertwined such that they continuously exchange data to reveal useful information about the overall system. Such class of CPSs need to be robust to various types of disturbances, such as faulty sensors and model discrepancies, since the interplay between the physical plant's operation and digital twin's simulation may lead to undesirable or even destructive effect. To address this problem, this paper introduces a flexible anomaly detection framework for monitoring anomalous behaviours in digital twin based CPSs. In particular, our approach integrates both the digital twin and data-driven techniques that detect and classify anomalous behaviours due to modelling errors (e.g. incomplete models) and sensor and physical system's faults. The framework can be deployed to any general CPSs without the full knowledge of the digital twin's internal model. Therefore, our method is amenable to various types of digital twin implementations that enhance the traditional data-driven anomaly detection mechanism. We demonstrate the performance of our approach using the Tennessee Eastman Process model. The experimental result shows our approach is able to effectively detect and classify anomaly sources from the physical plant, sensor and digital twin, even in the situation when a certain combination of multiple anomalies occur simultaneously.