Knowledge-Driven Framework for Maintenance Error Prevention Design

Maintenance errors often originate from early design decisions, such as ambiguous interface specifications, unclear assembly sequences, or improper component interactions, which can significantly reduce maintenance efficiency, increase operational costs, and compromise overall system reliability. Existing approaches to maintenance error prevention primarily rely on expert knowledge and past experience, lacking a systematic, formalized, and reusable mechanism for representing, reasoning, and managing maintenance-related knowledge throughout the design process. To address this limitation, this paper proposes a comprehensive knowledge-driven framework for maintenance error prevention design. The framework integrates systematic domain knowledge modeling, ontology-based representation, and a hybrid reasoning mechanism to support the proactive identification of potential errors at the early design stage. Concurrently, Model-Based Definition (MBD) annotations within 3D CAD models are semantically enriched and linked to ontology entities. By employing a hybrid semantic reasoning approach that integrates Description Logic (DL) subsumption for implicit structural classification with Semantic Web Rule Language (SWRL) for unstructured constraint validation, the framework establishes logical connections between design intent and maintenance requirements. This enables the automated detection of multidimensional risks, such as assembly sequence omissions and spatial clearance conflicts, while providing actionable design improvement recommendations. Furthermore, maintenance feedback, historical failure data, and operational observations are aligned with the ontology to iteratively refine rules and expand the knowledge repository. This approach creates an effective semantic bridge between design knowledge and practical maintenance experience, facilitating knowledge-based error prevention and supporting enhanced product reliability and maintainability throughout the entire lifecycle.

Keywords: Maintenance error prevention,Ontology-based reasoning, Model-Based Definition (MBD), Knowledge driven design.