Digital Twin-driven online anomaly detection for an automation system based on edge intelligence

Accurate anomaly detection is critical to the early detection of potential failures of industrial systems and proactive maintenance schedule management. There are some existing challenges to achieve efficient and reliable anomaly detection of an automation system: (1) transmitting large amounts of data collected from the system to data processing components; (2) applying both historical data and real-time data for anomaly detection. This paper proposes a novel Digital Twin-driven anomaly detection framework that enables real-time health monitoring of industrial systems and anomaly prediction. Our framework, adopting the visionary edge AI or edge intelligence (EI) philosophy, provides a feasible approach to ensuring high-performance anomaly detection via implementing Digital Twin technologies in a dynamic industrial edge/cloud network. Edge-based Digital Twin allows efficient data processing by providing computing and storage capabilities on edge devices. A proof-ofconcept prototype is developed on a LiBr absorption chiller to demonstrate the framework and technologies' feasibility. The case study shows that the proposed method can detect anomalies at an early stage.

Automated summary

This paper introduces a novel Digital Twin-driven anomaly detection framework for real-time health monitoring and anomaly prediction of industrial systems using edge intelligence technology. The adoption of Digital Twin technology enables efficient data processing and high-performance anomaly detection in industrial systems.