Exploration of Flight State and Control System Parameters for Prediction of Helicopter Loads via Gamma Test and Machine Learning Techniques

Abstract

AbstractAccurate estimation of helicopter component loads is an important goal for life cycle management and life extension efforts. In this research, estimates of helicopter dynamic loads were achieved through a combination of statistical and machine learning (computational intelligence) techniques. Estimates for the main rotor normal bending (MRNBX) loads on the Sikorsky S-70A-9 Black Hawk helicopter during two flight conditions (full speed forward level flight and rolling left pullout at 1.5g) were generated from an input set comprising 30 standard flight state and control system (FSCS) parameters. Data exploration using principal component analysis and multi-objective optimization of Gamma test parameters generated reduced subsets of predictors. These subsets were used to estimate MRNBX using neural network models trained by deterministic and evolutionary computation techniques. Reasonably accurate and correlated models were obtained using the subsets of the multi-objective optimization, also allowing some insight into the relationship between MRNBX and the 30 FSCS parameters.KeywordsRoot Mean Square ErrorParticle Swarm OptimizationDifferential EvolutionFlight ConditionMain RotorThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.