| Téléchargement | - Voir le manuscrit accepté : Towards conservative helicopter loads prediction using computational intelligence techniques (PDF, 682 Kio)
|
|---|
| DOI | Trouver le DOI : https://doi.org/10.1109/IJCNN.2012.6252624 |
|---|
| Auteur | Rechercher : Valdes, Julio J.1; Rechercher : Cheung, Catherine2; Rechercher : Li, Matthew2 |
|---|
| Affiliation | - Conseil national de recherches du Canada. Institut de technologie de l'information du CNRC
- Conseil national de recherches du Canada. Institut de recherche aérospatiale du CNRC
|
|---|
| Format | Texte, Article |
|---|
| Conférence | IEEE 2012 International Joint Conference on Neural Networks (IJCNN 2012), June 10-15, 2012, Brisbane, Australia |
|---|
| Résumé | 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. |
|---|
| Date de publication | 2012-06-15 |
|---|
| Dans | |
|---|
| Langue | anglais |
|---|
| Publications évaluées par des pairs | Oui |
|---|
| Numéro NPARC | 20847546 |
|---|
| Exporter la notice | Exporter en format RIS |
|---|
| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
|---|
| Identificateur de l’enregistrement | 10db696b-8a8c-4547-b93c-48837b7cb311 |
|---|
| Enregistrement créé | 2012-10-22 |
|---|
| Enregistrement modifié | 2020-04-21 |
|---|
