Towards conservative helicopter loads prediction using computational intelligence techniques

Airframe structural integrity assessment is a major activity for all helicopter operators. The accurate estimation of component loads is an important element in life cycle management and life extension efforts. This paper explores continued efforts to utilize a wide variety of computational intelligence techniques to estimate some of these helicopter dynamic loads. Estimates for two main rotor sensors (main rotor normal bending and pushrod axial load) on the Australian Black Hawk helicopter were generated from an input set that consisted of thirty standard flight state and control system parameters. These estimates were produced for two flight conditions: full speed forward level flight and left rolling pullout at 1.5g. Two sampling schemes were attempted, specifically k-leaders sampling and a biased sampling scheme. Ensembles were constructed from the top performing models that used conjugate gradient, Levenberg-Marquardt (LM), extreme learning machines, and particle swarm optimization (PSO) as the learning method. Hybrid and memetic approaches combining the deterministic optimization and evolutionary computation techniques were also explored. The results of this work show that using a biased sampling scheme significantly improved the predictions, particularly at the peak values of the target signal. Hybrid models using PSO and LM learning provided accurate and correlated predictions for the main rotor loads in both flight conditions.