A Multivariate Time-Series Segmentation Framework for Flight Condition Recognition

Helicopters usage monitoring has gained significant attention in recent years, due to the safety and cost management implications. At its core there is the flight condition recognition algorithm, which enables to detect the maneuvers carried out by the aircraft through on-board sensors measurements. In this work, we propose a multivariate time-series segmentation framework, which uses supervised learning algorithms, sliding windows, and stacking ensembles to produce reliable estimates of the flown flight regimes. We validate the proposed approach on a large dataset of 460 labeled load flights from two distinct helicopter models, demonstrating its efficacy in predicting a range of 49 different maneuver types.

Automated summary

Helicopters usage monitoring has become important due to safety and cost management implications. This study proposes a multivariate time-series segmentation framework using supervised learning algorithms, sliding windows, and stacking ensembles to accurately predict flight regimes. The approach is validated on a large dataset of labeled load flights from two helicopter models, demonstrating its efficacy in predicting 49 different maneuver types.