A joint EPSRC-funded Impact Acceleration Account (IAA) project titled “Big-data: improving aircraft performance” has been funded to exploit the findings and methods we’ve developed in the Airbus In-Service department.
This six-month long project led by Dr Lei Shi and Prof. Linda Newnes aims to embed the approaches created within the project, including big-data analytics, trend analysis and autonomic computing, to interrogate and categorise aircraft wing In-Service projects. The research at the University of Bath has demonstrated that it is possible to automatically predict the complexity, duration and cost of such repair cases. This has been achieved through interrogating 10,000+ historical projects to create and validate the proposed approaches. Initial tests have been completed to ascertain whether the approaches can be used on the ‘live’ data from the Airbus In-Service workflow system.
Our overall aims are to develop the processes through on-site development and testing, to make the approaches self-sustaining, and to assist the in-service teams with their decision-making.
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