Method for Knowledge-Based Helicopter Track and Balance

Abstract

Abstract : The aim of the project was to develop an efficient method of helicopter rotor tuning (track and balance) to cope with the potential nonlinearity of the process and to account for the vibration noise. Toward this goal, two methods have been developed. The first method relies on an interval model to represent the range of effect of blade adjustments on helicopter vibration and incorporates learning to adapt the coefficients of the interval model. The coefficients of the model are initially defined according to sensitivity coefficients between the blade adjustments and helicopter vibration, to include the a priori' knowledge of the process These coefficients are subsequently transformed into intervals and updated after each tuning iteration to improve the model's estimation accuracy. The second method of rotor tuning uses a probability model to maximize the likelihood of success of the selected blade adjustments. The underlying model in this method consists of two segments: a linear segment to include the sensitivity coefficients between the blade adjustments and helicopter vibration, and a stochastic segment to represent the probability densities of the vibration components. Based on this model, the blade adjustments with the maximal probability of generating acceptable vibration are selected as recommended adjustments. The effectiveness of the two methods are evaluated in simulation using a series of neural networks trained with actual vibration data. The results indicate that the developed methods improve performance according to several criteria representing various aspects of track and balance.