Advanced Data Methods for Improved Tiltrotor Test and Design (ADMITTED)

Empowering rotorcraft testing and design by Big Data and Artificial Intelligence

Modern aircrafts are based on a rich set of on-board computers supporting many different tasks, capable of recording hundreds of parameters during flight. This allows not only the investigation of problems occurred during flights, such as accidents or a serious incident, but also provides the opportunity to use the recorded data to predict future aircraft behaviour. This can be done with precise analyses of the recorded data, in order to identify possible hazardous behaviours and developing procedures to mitigate the problems before they actually occur. 
ADMITTED will build a novel set of algorithms and methods that will enable to organise, visualise and process raw flight data.

 This will allow to: 

  • Discover anomalies in sensor data 
  • Detect and classify the type of flight condition 
  • Automatically recognise standard manoeuvres and re-occurring events 

Beside this a set of knowledge discovery algorithms will be devised and implemented. With this tool it would be possible to extract and incorporate useful content from different and heterogeneous data sources such as Meteorological data, Operational data and Maintenance data.