Development of a runtime-condition model for proactive intelligent products using knowledge graphs and embedding

Abstract

Modern manufacturing processes’ increasing complexity and variability demand advanced systems capable of real-time monitoring, adaptability, and data-driven decision-making. This paper introduces a novel runtime condition model to enhance interoperability, data integration, and decision support within intelligent manufacturing environments. The model encapsulates key manufacturing elements, including asset management, relationships, key performance indicators (KPIs), capabilities, data structures, constraints, and configurations. A key innovation is the integration of a knowledge graph enriched with embedding techniques, enabling the inference of missing relationships, dynamic reasoning, and predictive analytics.

The proposed model was validated through a case study conducted in collaboration with TQC Automation Ltd., using their MicroApplication Leak Test System (MALT). A dataset of over 9,000 unique test configurations demonstrated the model’s capabilities in representing runtime conditions, managing operational parameters, and optimising test configurations. The enriched knowledge graph facilitated advanced analyses, providing actionable insights into test outcomes and enabling proactive decision-making.

Empirical results showcase the model’s ability to harmonise diverse data sources, infer missing connections, and improve runtime adaptability. This study highlights the potential of combining runtime modelling with knowledge graphs to address the challenges of modern manufacturing. Future research will explore the model’s application to additional domains, integration with larger datasets, and the use of machine learning for enhanced predictive capabilities.