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Graph-based information management for retrofitting long-living assets
Citation Link: https://doi.org/10.15480/882.14139
Publikationstyp
Journal Article
Date Issued
2024-12-20
Sprache
English
TORE-DOI
Journal
Volume
5
Article Number
e46
Citation
Cambridge University Press 5: e46 (2024)
Publisher DOI
Scopus ID
Publisher
Cambridge University Press
Many documents are produced over the years of managing assets, particularly those with long lifespans. However, during this time, the assets may deviate from their original as-designed or as-built state. This presents a significant challenge for tasks that occur in later life phases but require precise knowledge of the asset, such as retrofit, where the assets are equipped with new components. For a third party who is neither the original manufacturer nor the operator, obtaining a comprehensive understanding of the asset can be a tedious process, as this requires going through all available but often fragmented information and documents. While common knowledge regarding the domain or general type of asset can be helpful, it is often based on the experiences of engineers and is, therefore, only implicitly available. This article presents a graph-based information management system that complements traditional PLM systems and helps connect fragments by utilizing generic information about assets. To achieve this, techniques from systems engineering and data science are used. The overarching management platform also includes geometric analyses and operations that can be performed with geometric and product information extracted from STEP files. While the management itself is first described generically, it is also later applied to cabin retrofit in aviation. A mock-up of an Airbus A320 is utilized as the case study to demonstrate further how the platform can provide benefits for retrofitting such long-living assets.
Subjects
Aircraft Cabin Retrofit
Data Access
Data Analysis
Data Handling
Graph Database
DDC Class
600: Technology
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graph-based-information-management-for-retrofitting-long-living-assets.pdf
Type
Main Article
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1.66 MB
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