Process Mining for Digital Twin Development of Industrial Cyber-Physical Systems

Abstract

Digital twin development of industrial cyber-physical systems requires modeling, simulation, and monitoring to provide accurate digital replicas mimicking system dynamics. To this aim, modern solutions employ data-driven approaches to capture normal system dynamics using historical data, and anomaly detection using run-time data. However, these approaches are typically process-agnostic and have limited explainability, which negatively impacts trustworthiness. Hence, we propose a novel framework for the digital twin development of industrial cyber-physical systems based on process mining, which connects data-driven and process-based analyses. The framework implements an offline phase that systematically identifies the most suitable process model to capture normal dynamics and simulates faulty dynamics for what-if analysis. The subsequent online phase involves run-time monitoring for anomaly detection. We develop a proof-of-concept application of the framework addressing a water distribution case study that allows physical fault injection. Results highlight the importance of adopting high-quality process models to significantly improve anomaly detection and time performance.

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