A Review of an Ontology-Based Digital Twin to Enable Condition-Based Maintenance for Aircraft Operations
Source abstract
Abstract
The concept of digital twins has been studied for over two decades and the core tenet lies in it being a “digital representation of a connected physical object”. Utilization of digital twins promises to enable superior decision-making, enhanced operational understanding and future predictions to enable levels of Condition Based Maintenance (CBM) through Integrated Vehicle Health Management (IVHM) which exceeds existing capabilities. Digital twins are being embraced by many industries, including aviation, and are often depicted as electronic images of an asset of interest. However, in a less visually appealing manner, they can also be described simply as a collection of data in an organized and easily accessible format from across the lifecycle which describes a feature that addresses a specific use case. This review demonstrates how the creation and maintenance of digital twins will play a critical role in enhancing IVHM to enable CBM within the aerospace industry. Through a literature review, this paper demonstrates the need for digital twins, of a sufficient level of fidelity, to facilitate the transition to being condition based through deeper levels of operational and component understanding. It emphasizes how detailed knowledge, represented through ontologies, regarding component design, manufacturing, and operational history aid in achieving the desired fidelity levels. By synthesizing insights from various industries with a focus on aerospace applications, this paper aims to provide a comprehensive understanding, focused on the aviation industry, of digital twin definitions, their creation processes, fidelity measurement, and their implications for CBM, while acknowledging the limitations of the current research landscape.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Technology Assessment and Management · Digital Transformation in Industry · Manufacturing Process and Optimization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
IVHM Centre, Faculty of Engineering and Applied Sciences, Cranfield University, Bedford MK43 0AL, UK
Department of Aeronautics and Astronautics, School of Engineering, University of Southampton, Southampton SO17 1BJ, UK
References (100+)
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