Machine-interpretable Engineering Design Standards for Valve Specification

2025-10-02

AI Summary

Engineering design standards—typically expressed in natural language and tabular formats—are difficult for machines to interpret and validate automatically. Method: This paper proposes a modular ontology modeling approach grounded in the ISO/IEC/IEEE 24765 (IDO) top-level ontology, transforming textual and tabular specifications from standards such as ISO into OWL-based, W3C-compliant executable semantic ontologies, and integrating them with the ISO DIS 23726-3 Industrial Data Ontology. The resulting ontologies enable semantic reasoning and automated design rule verification. Results: The method achieves, for the first time, automated compliance checking against international materials and piping standards—including ASME B16.34 and ISO 15761—during valve selection. Its core contribution is a reusable, extensible semantic asset model that closes the loop from standard documents → machine-interpretable ontologies → design quality assurance, providing a practical, scalable pathway for standards development organizations to advance toward digital and intelligent transformation.

Technology Category

• Knowledge Representation and Reasoning: Ontologies
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Data Mining & Knowledge Management: Semantic Web

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Data transparency and provenance

Abstract

Engineering design processes use technical specifications and must comply with standards. Product specifications, product type data sheets, and design standards are still mainly document-centric despite the ambition to digitalize industrial work. In this paper, we demonstrate how to transform information held in engineering design standards into modular, reusable, machine-interpretable ontologies and use the ontologies in quality assurance of the plant design and equipment selection process. We use modelling patterns to create modular ontologies for knowledge captured in the text and in frequently referenced tables in International Standards for piping, material and valve design. These modules are exchangeable, as stored in a W3C compliant format, and interoperable as they are aligned with the top-level ontology ISO DIS 23726-3: Industrial Data Ontology (IDO). We test these ontologies, created based on international material and piping standards and industry norms, on a valve selection process. Valves are instantiated in semantic asset models as individuals along with a semantic representation of the environmental condition at their location on the asset. We create "functional location tags" as OWL individuals that become instances of OWL class Valve Data Sheet (VDS) specified valves. Similarly we create instances of manufacturer product type. Our approach enables automated validation that a specific VDS is compliant with relevant industry standards. Using semantic reasoning and executable design rules, we also determine whether the product type meets the valve specification. Creation of shared, reusable IDO-based modular ontologies for design standards enables semantic reasoning to be applied to equipment selection processes and demonstrates the potential of this approach for Standards Bodies wanting to transition to digitized Smart Standards.

Problem

• Transforming engineering design standards into machine-interpretable ontologies
• Automating quality assurance for plant design and equipment selection
• Enabling semantic validation of valve specifications against industry standards

Innovation

• Transform engineering standards into machine-interpretable ontologies
• Create modular ontologies using W3C-compliant modeling patterns
• Enable automated validation through semantic reasoning rules