SafeClouds have contributed to aviation safety across five different threads:
- Precursor analysis: Existing data analytic methods facilitate historical analysis in monitoring relevant safety events (e.g. unstable approaches, ground proximity or airprox events). SafeClouds goes further by proposing a formal methodology to analyse precursors of those events. The precursor analysis, performed through machine learning techniques, has been matured to TRL5 for a number of safety-relevant scenarios, like unstable approaches or runway safety. The process has been automated, from data fusion and labelling to algorithm training and reporting, through a visual interface.
- Predictive analytics: Powerful predictive analytics has complemented historical analysis to help forecast the likelihood of a particular event in certain conditions. Current methodologies do not offer any predictive capability. SafeClouds has developed and presented a concept for understanding traffic evolution at different time horizons and the related safety implications. For instance, SafeClouds has proposed a concept to predict the likelihood of an unstable approach 30 seconds before the event, potentially allowing the design of new cabin crew procedures, or, with cooperation from ATCs, a concept to predict when an unstable approach is likely to happen. Predictive analytics can complement the current reporting systems.
- Data fusion for reporting: Data fusion is very limited in aviation. For instance, airline SMS departments can analyse their safety events from their perspective, using their sources of data. SafeClouds has opened the door to including meteorological reports or surrounding traffic data, a major breakthrough for the analytics of safety events. Similarly, runway safety, normally only analysed with airport data, can be improved through data fusion with airline data.
- Automatic safety monitoring: SafeClouds has presented DataBeacon, a data platform that automates the data pipeline including data ingestion, cleaning, de-identification, fusion, labelling for specific events, training, results and visualisation. This concept is much more advanced than existing data processes which require many manual steps and disconnected tools. This automatic safety monitoring paradigm is built-in in DataBeacon and contributes to future reporting systems.
- Anomaly detection: SafeClouds has made some very important steps in using neural network architectures for finding anomalous flights using FDM data. This is a novel approach with very few mentions in literature. SafeClouds has proved the value of deep learning by demonstrating the detection of rare safety events, such as Unstable Approaches.
