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Towards conservative helicopter loads prediction using computational intelligence techniques

From National Research Council Canada

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DOIResolve DOI: https://doi.org/10.1109/IJCNN.2012.6252624
AuthorSearch for: 1; Search for: 2; Search for: 2
Affiliation
  1. National Research Council of Canada. NRC Institute for Information Technology
  2. National Research Council of Canada. NRC Institute for Aerospace Research
FormatText, Article
ConferenceIEEE 2012 International Joint Conference on Neural Networks (IJCNN 2012), June 10-15, 2012, Brisbane, Australia
Abstract
Airframe structural integrity assessment is a major activity for all helicopter operators. The accurate estimation of component loads is an important element in life cycle management and life extension efforts. This paper explores continued efforts to utilize a wide variety of computational intelligence techniques to estimate some of these helicopter dynamic loads. Estimates for two main rotor sensors (main rotor normal bending and pushrod axial load) on the Australian Black Hawk helicopter were generated from an input set that consisted of thirty standard flight state and control system parameters. These estimates were produced for two flight conditions: full speed forward level flight and left rolling pullout at 1.5g. Two sampling schemes were attempted, specifically k-leaders sampling and a biased sampling scheme. Ensembles were constructed from the top performing models that used conjugate gradient, Levenberg-Marquardt (LM), extreme learning machines, and particle swarm optimization (PSO) as the learning method. Hybrid and memetic approaches combining the deterministic optimization and evolutionary computation techniques were also explored. The results of this work show that using a biased sampling scheme significantly improved the predictions, particularly at the peak values of the target signal. Hybrid models using PSO and LM learning provided accurate and correlated predictions for the main rotor loads in both flight conditions.
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  • Proceedings of the IEEE 2012 World Congress on Computational Intelligence, IEEE 2012 International Joint Conference on Neural Networks (15 June 2012): 1–8.
LanguageEnglish
Peer reviewedYes
NPARC number20847546
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Record identifier10db696b-8a8c-4547-b93c-48837b7cb311
Record created2012-10-22
Record modified2020-04-21
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