Rotorcraft Flight Regime Recognition in Complex Maneuvers Using Adaptive Extended Kalman Filter

The retirement time of helicopter components can be extended using the health and usage monitoring system (HUMS) and revisiting the flight spectrum with enough precision through fatigue damage analysis. The recognition of flight regimes for air vehicles with complex maneuvers is a major problem in determining the experienced spectrum. The relationship between flight variables and regimes is complex and coupled during a mission profile with multiple maneuvers that needs more efficient algorithms. In this paper, a logical framework is developed to identify the complex maneuvers based on the qualitative and descriptive interpretation of each maneuver by the pilot. The regime recognition criteria are implemented in the algorithm using an adaptive extended Kalman filter with both flight instruments and control movements as the measurements. The proposed algorithm classifies the flight regimes into the standard fatigue spectrum while needing no large amount of training data and not being sensitive to pilot behavior and mission variations. The proposed regime recognition algorithm is evaluated through extensive simulation of a comprehensive dynamic model of helicopter flight. The results show the improved accuracy of the proposed method in comparison with that of previous ones, while the proposed method is easier to embed in HUMS software without exhaustive training.

Automated summary

The retirement time of helicopter components can be extended using HUMS and fatigue damage analysis. This study proposes a logical framework to identify complex maneuvers and classify flight regimes based on flight data and control information. The algorithm shows high accuracy and ease of embedding in HUMS software.