FDM software currently makes use of maximum thresholds, which are flagged whenever exceeded. The data would require further investigation to establish if procedures or safety has been compromised. However, the ever-increasing sensors installation aboard aircraft, makes flight data analysis laborious and complex.
Current techniques not only miss the ability to establish trends and patterns prior to developing faults. Furthermore, once an anomaly is established, a thorough investigative effort is required to determine the contributory factors leading to the anomaly. The latter heavily relies on the human expert but can be subjective to the trained user.
Project WAGE continues on previous research which applied machine learning techniques to detect anomalies in aircraft data. Project WAGE aims to establish context and the origins of the anomalies. Such contributory factors may include weather, geographical, and engineering issues. This offers valuable insight for improved safety and flight operations.
'The project aims to provide valuable information which in future would lead to optimise flight trajectories (for example when hazardous weather patterns are known and expected), and maintenance schedules with the minimum impact on the fleet operations. This would in turn result in added value in improved aviation safety and airline economic advantages,' stated Dr Robert Camilleri who is leading the project with the contribution of Dr Gianluca Valentino.
Dr Camilleri said that Project WAGE is a three-year research collaboration between the Institute of Aerospace Technologies, the Department of Communications & Computer Engineering within the Faculty of ICT and our industrial partner QuAero Ltd.
WAGE (R&I-2019-009-T) is a EUR 195,000 project financed by the Malta Council for Science & Technology, for and on behalf of the Foundation for Science and Technology, through the FUSION: R&I Technology Development Programme.
