Flight Data Monitoring (FDM) programmes have become a key part of every major airline’s safety management system. They are primarily based on learning from unwanted deviations in flight parameters encountered during normal flight operations. Owing to its unique nature, anomaly detection of FDM presents distinct problem complexities from the majority of analytical and learning tasks. This methodology, while useful, concentrates only on a small part of the operation, leaving most of the data unprocessed, and does not allow for analysing events that had the potential to go wrong but were recovered in time by the crews. This research focused on analysing an FDM dataset of 1332 approaches between January 2018 and July 2022 at Tenerife South Airport (Spain), where there is a known phenomenon of increasing headwinds during the final approach. The flights were clustered using self-organising maps (SOM) by patterns of increasing headwinds, and the clusters were assessed in terms of clustering performance. The clusters were well differentiated. A further comparison between the results from the airline showed that 88 flights were affected by wind shifts, while 27 flights were picked up by the airline. The results demonstrate that SOMs are a meaningful tool for clustering flight data and can complement the current FDM analysis methodology. Combining both methodologies could shift FDM data analysis to look beyond exceedances into what went well, thus shifting the FDM paradigm towards a more safety-II-based method.

The purpose of this paper is to group the flight data phases based on the sensor readings that are most distinctive and to create a representation of the higher-dimensional input space as a two-dimensional cluster map. The research design includes a self-organising map framework that provides spatially organised representations of flight signal features and abstractions. Flight data are mapped on a topology-preserving organisation that describes the similarity of their content. The findings reveal that there is a significant correlation between monitored flight data signals and given flight data phases. In addition, the clusters of flight regimes can be determined and observed on the maps. This suggests that further flight data processing schemes can use the same data marking and mapping themes regarding flight phases when working on a regime basis. The contribution of the research is the grouping of real data flows produced by in-flight sensors for aircraft monitoring purposes, thus visualising the evolution of the signal monitored on a real aircraft.