Visualising flight regimes using self-organising maps

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.

Automated summary

The purpose of this paper is to group flight data phases based on distinctive sensor readings and represent the input space as a two-dimensional cluster map. The research design uses a self-organising map framework to provide organised representations of flight signal features. The findings show a significant correlation between monitored flight data signals and flight phases, and the clusters of flight regimes can be determined and observed on the maps. The contribution of this research is the grouping of real data flows for aircraft monitoring and visualising the evolution of monitored signals on a real aircraft.