Identification of helicopter component loads using multiple regression

Abstract

Multiple regression analysis of helicopter flight data is used to develop prediction models for rotating system component loads from parameters measured in the fixed system. The data base that is analyzed contains load measurements for a helicopter performing several types of flight maneuvers, including symmetric pullouts, rolling pullouts, climbing turns, and level flight. The data are divided into two parts: one for model development and one to serve as a blind test of the model. For steady level flight, linear and nonlinear regression analyses are performed to predict main rotor pushrod and blade normal bending vibratory loads. Correlations above 95% were achieved on the test data for the steady level flight condition. For comparison, analytical results calculated using the CAMRAD/JA rotor analysis computer code for the helicopter in level flight are included. Regression models to predict vibratory loads during maneuvering flight are also developed. Evaluations on the test data indicate that correlations ranging from 79 to 95% are possible for the types of maneuvers contained in the data base.