Supporting Digital Twins for the Retrofit in Aviation by a Model-Driven Data Handling - Fabian Niklas Laukotka; Dieter Krause (Systems, 2023)
Aviation is characterized by many stakeholders, long lifespans of its assets, and high requirements regarding safety, security, and documentation. To meet these requirements as well as customer needs, aircraft are regularly retrofitted with new cabins. During the planning and execution of this cabin retrofit, handling the needed and available data poses a challenge to the engineers. While much of the required data is available in some form, generally there is a lack of a digitally usable dataset of the specific aircraft—a virtual representation of the physical asset is missing. To support the implementation of such a digital twin and, thus, the overall process of retrofitting aircraft, an approach to model-driven data handling tailored to the unique circumstances and requirements of aviation is introduced. The methodology consists of a combination of systems engineering and data science techniques framed by an overarching procedure that iteratively creates and enhances a digitally accessible dataset of the relevant data, hence supporting the retrofit engineers by easing access to needed information. Besides the presentation of the research background and the methodology, a simplified example is shown, demonstrating the approach using abstracted but realistic information provided by partners from the industry.