Traditional inspection approaches often face challenges related to integration and interoperability across system boundaries. To overcome these issues, knowledge-based systems have increasingly been adopted for their ability to formalize domain expertise and support data-driven decision-making. However, a persistent gap remains between knowledge engineering and system modeling, which hinders information flow and semantic consistency throughout the system lifecycle. To bridge this gap, this study introduces a domain knowledge-enhanced Model-Based Systems Engineering (MBSE) framework that establishes a semantic connection between the system design phase and operational phase. The approach embeds a domain inspection ontology into the MBSE workflow at the metamodel level through the construction of domain-specific metamodels that integrate ontological concepts into core modeling constructs. Once developed, these semantically enriched system models are systematically transformed into application ontologies, which serve as schemas for operational knowledge bases. The framework is validated through a real-world overhead crane inspection case study. The evaluation demonstrates enhanced lifecycle traceability, efficient cross-domain interoperability, reliable rule-based reasoning, and reduced maintenance effort. Overall, this work contributes a unified methodology that bridges system modeling and operational knowledge for intelligent inspection systems. • Proposes an MBSE framework that integrates domain knowledge at the metamodel level. • Creates a semantic bridge from design-time system models to operational knowledge bases. • Automates the generation of application ontologies from semantically enriched system models. • Feasibility is demonstrated via an overhead crane inspection case study.
Yang et al. (Sun,) studied this question.
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