NEURAL NETWORK MODELLING OF OSCILLATORY LOADS AND FATIGUE DAMAGE ESTIMATION OF HELICOPTER COMPONENTS

A neural network for the prediction of oscillatory loads used for on-line health monitoring of flight critical components in an AH-64A helicopter is described. The neural network is used to demonstrate the potential for estimating loads in the rotor system from fixed-system information. Estimates of the range of the pitch link load are determined by the neural network from roll, pitch, and yaw rates, airspeed, and other fixed-system information measured by the flight control computer on the helicopter. The predicted load range is then used to estimate fatigue damage to the pitch link. Actual flight loads data from an AH-64A helicopter are used to demonstrate the process. The predicted load ranges agree well with measured values for both training and test data. A linear model is also used to predict the load ranges, and its accuracy is noticeably worse than that of the neural network, especially at higher load values that cause fatigue damage. This demonstrates the necessity of the non-linear modelling capabilities of the neural network for this problem.