Evolutionary computation methods for helicopter loads estimation

  1. (PDF, 437 KB)
DOIResolve DOI: http://doi.org/10.1109/CEC.2011.5949805
AuthorSearch for: ; Search for: ; Search for:
Proceedings titleEvolutionary Computation (CEC), 2011 IEEE Congress on
Conference2011 IEEE Congress on Evolutionary Computation (CEC), June 5-8, 2011, New Orleans, LA, USA
Pages15891596; # of pages: 8
SubjectUsage monitoring, Evolutionary computation methods, Artificial neural networks , Computational modeling , Genetic algorithms , Helicopters , Input variables , Predictive models , Training
AbstractThe accurate estimation of component loads in a helicopter is an important goal for life cycle management and life extension efforts. This paper explores the use of evolutionary computational methods to help estimate some of these helicopter dynamic loads. Thirty standard time-dependent flight state and control system parameters were used to construct a set of 180 input variables to estimate the main rotor blade normal bending during forward level flight at full speed. Evolutionary computation methods (single and multi-objective genetic algorithms) optimizing residual variance, gradient, and number of predictor variables were employed to find subsets of the input variables with modeling potential. Clustering was used for composing a statistically representative training set. Machine learning techniques were applied for prediction of the main rotor blade normal bending involving neural networks, model trees (black and white box techniques) and their ensemble models. The results from this work demonstrate that reasonably accurate models for predicting component loads can be obtained using smaller subsets of predictor variables found by evolutionary computation based approaches.
Publication date
AffiliationNRC Institute for Information Technology; NRC Institute for Aerospace Research; National Research Council Canada
Peer reviewedNo
NPARC number18150445
Export citationExport as RIS
Report a correctionReport a correction
Record identifier01c32fe6-0056-4670-aafc-94022f56c83f
Record created2011-06-24
Record modified2016-05-09
Bookmark and share
  • Share this page with Facebook (Opens in a new window)
  • Share this page with Twitter (Opens in a new window)
  • Share this page with Google+ (Opens in a new window)
  • Share this page with Delicious (Opens in a new window)