Helicopter Track and Balance With Artificial Neural Nets

[+] Author and Article Information
Howard J. Taitel, Kourosh Danai

Department of Mechanical Engineering, University of Massachusetts, Amherst, MA 01003

David Gauthier

Sikorsky Aircraft, Stratford, CT 06601

J. Dyn. Sys., Meas., Control 117(2), 226-231 (Jun 01, 1995) (6 pages) doi:10.1115/1.2835183 History: Received September 01, 1992; Online January 22, 2008


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.

Copyright © 1995 by The American Society of Mechanical Engineers
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