Work

Linking aircraft structural testing data through semantic technologies

Year: 2024

Type: article

Abstract: Difficulties in data integration and interoperability in aircraft structural testing arise from disparate systems and data silos. A modular Point-Based-Measurement (PBM) ontology was created to address these inefficiencies by enabling systematic domain knowledge linkage across systems. This study introduces a new Digital Image Correlation (DIC) module for PBM, developed in collaboration with Airbus and Zeiss. Using a DIC dataset, the ontology was validated through SPARQL queries, highlighting improved data accessibility and data linking. The validation process not only confirmed the ontology's effectiveness but also showcased its potential to improve operator efficiency. Preliminary results demonstrate enhanced querying capabilities and streamlined testing workflows. This underscores the benefits of semantic unification in structural testing and importance of increased industry collaboration for broader adoption. The work also demonstrates that semantic technologies, such as ontologies and knowledge graphs, provide key solutions to the challenges of FAIR data integration, thereby enabling knowledge-driven innovations like digital twins.

Source: Measurement Sensors

DOI: 10.1016/j.measen.2024.101457

Authors: Michael Chrubasik, Hannah Strassburg, Emily Rolfe, Ramani Sankar Rajendran, Jean-Laurent Hippolyte

Institutions: National Physical Laboratory, Airbus (United Kingdom), Carl Zeiss (United Kingdom)

Language: English

Topic: Semantic Web and Ontologies

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $1,800