ENACT Contribution au Jumeau Numérique Cognitif pour la gestion de la santé et le pronostic des systèmes de production par combinaison d'apprentissage automatique et d'ontologies

Keywords

cognitive digital twin, artificial intelligence, ontology, machine learning, predictive maintenance, productions systems

Subject details

Digital Twin (DT) technology has gained significant traction in manufacturing, particularly for predictive maintenance and Prognostic and Health Management (PM&PHM). Traditional DTs mainly focus on individual components like bearings or gearboxes, limiting their application to system-level maintenance. Recent research suggests integrating multiple DTs to enhance PM&PHM, bridging this gap. A major advancement is the Cognitive Digital Twin (CDT), which extends DTs by semantically interlinking multiple digital models for collaborative intelligence. Unlike traditional DTs, CDTs incorporate cognitive capabilities such as perception, reasoning, memory, learning, and problem-solving. Like DTs, they consist of (1) a physical entity, (2) a digital representation, and (3) bidirectional connections. However, CDTs also integrate multiple DT models with unified semantics, enabling continuous evolution alongside their physical counterparts. The CDT concept introduces a more intelligent and autonomous approach to system monitoring, supported by AI, semantic technologies, Industrial IoT (IIoT), and ubiquitous sensing. A widely accepted definition describes a CDT as a digital representation of a physical system, augmented with cognitive abilities to support autonomous decision-making. It interlinks multiple digital models across different lifecycle phases, ensuring adaptability and intelligence in real time. Despite promising developments, no comprehensive CDT framework currently exists for PM&PHM in production systems. Key enabling technologies—such as ontologies, knowledge graphs, and machine learning—remain underexplored in the context of DT cognition. Addressing this gap requires a structured approach to CDT design. This PhD research focuses on: Defining a CDT architecture and its key components for PM&PHM in production systems. Modeling and semantically integrating multiple DTs to facilitate collaborative intelligence across components and services. These objectives lead to two main research questions: RQ1: What are the essential elements of a CDT functional architecture for PM&PHM? RQ2: How can AI-driven techniques (ontologies, knowledge graphs, and ML) model and integrate multiple DTs for intelligent PHM in production systems? Solving RQ1 and RQ2 requires integrating ontologies and AI-driven approaches, including deep learning, into the PM&PHM lifecycle. A CDT framework must define interaction mechanisms between DTs while leveraging machine learning and semantic reasoning to enhance cognition. A promising direction is the fusion of machine learning, ontologies, and reasoning for predictive maintenance. Machine learning extracts patterns and failure indicators, while ontologies enhance data interpretation through semantic queries and rule-based reasoning. Integrating heterogeneous data sources improves failure prediction models, advancing CDT-driven PM&PHM. Data-driven AI allows DTs to detect and predict failures, ensuring high reliability. Meanwhile, ontologies and knowledge graphs enhance reasoning, inference, and interoperability. By combining data-driven and knowledge-based AI methods, CDTs can achieve both accuracy and interpretability, making them highly adaptable and context-aware. This hybrid approach is essential for scalable, reliable, and autonomous predictive maintenance solutions in complex industrial systems.