Helicopter Track and Balance With Artificial Neural Nets

Abstract

Before a helicopter leaves the plant, it needs to be tuned so that its vibrations meet the required specifications. Helicopter track and balance is currently performed based on “sensitivity coefficients” which have been developed statistically after years of production experience. The fundamental problem with using these sensitivity coefficients, however, is that they do not account for the nonlinear coupling between modifications or their effect on high amplitude vibrations. In order to ensure the reliability of these sensitivity coefficients, only a limited number of modifications are simultaneously applied. As such, a number of flights are performed before the aircraft is tuned, resulting in increased production and maintenance cost. In this paper, the application of feedforward neural nets coupled with back-propagation training is demonstrated to learn the nonlinear effect of modifications, so that the appropriate set of modifications can be selected in fewer iterations (flights). The effectiveness of this system of neural nets for track and balance is currently being investigated at the Sikorsky production line.