Artificial Intelligence Techniques With Digital Twin for Fault Diagnosis in Interconnected Systems: A Review
Imen Nakti, Majdi Mansouri, Rami Al‐Hmouz, Atef Khedher
Abstract
As industrial systems become more complex and interconnected, diagnosing faults accurately and in real time has become increasingly challenging. This paper explores how combining artificial intelligence with digital twin technology can address these challenges. We focus on developing hybrid Artificial intelligence models that leverage diverse data sources to enhance fault detection and diagnosis, enabling secure and distributed diagnostics. Digital twins, virtual models of physical systems, are shown to enhance predictive maintenance and decision-making by providing real-time system insights. What sets our work apart is the way we integrate these technologies to create scalable, adaptive, and context-aware diagnostic solutions. We demonstrate the potential of this approach across applications such as smart grids, manufacturing, and autonomous systems. Our goal is to provide researchers and practitioners with a practical and forward-looking framework for developing intelligent, reliable fault diagnosis systems in today’s data-rich industrial environments.