Skip to main content

A Probability‐Based Approach to Helicopter Rotor Tuning

Buy Article:

$22.00 plus tax (Refund Policy)

A method of helicopter rotor balance is introduced that 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 proposed method is evaluated in simulation using a series of neural networks trained with actual vibration data. The results indicate that the proposed method improves performance according to several criteria representing various aspects of track and balance.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Article Media
No Metrics

Document Type: Research Article

Affiliations: Department of Mechanical and Industrial Engineering, University of Massachusetts Amherst

Publication date: 2005-01-01

More about this publication?
  • The Journal of the American Helicopter Society is the world's only scientific journal dedicated to vertical flight technology. It is a peer-reviewed technical journal published quarterly by AHS International and presents innovative papers covering the state-of-the-art in all disciplines of rotorcraft design, research and development. (Please note that AHS members receive significant discounts on articles and subscriptions.)

    Journal subscribers who are AHS members log in here if you are not already logged in.

    Authors can find submission guidelines and related information on the AHS website.

  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more