An artificial neural network is trained using helicopter flight test data to predict rotor system component loads during high-speed maneuvering flight. Inputs to the network include control positions and aircraft state parameters. These parameters can be easily measured in the nonrotating system, i.e., the fuselage, and vary at a relatively low-frequency. A network design sensitivity study is conducted and several networks are developed for three loads; the rotor blade pushrod load, blade normal bending moment, and main-rotor damper load. Prediction accuracy is evaluated using a validation data set consisting of symmetric pull out maneuvers, rolling pull out maneuvers, and climbing turns not contained in the training data set. A traditional statistical approach, stepwise multiple linear regression, is also utilized and the two methods are compared and contrasted. Correlation coefficients from 84 % to 97 % are achievable using the neural network model for all three loads. Through a unified approach involving both neural network and statistical analysis greater accuracy and understanding of the neural network is attained.
Prediction of helicopter component loads using neural networks
Die Voraussage über die Belastung von Hubschrauberkomponenten unter Benutzung neutraler Netzwerke
1993
14 Seiten, 5 Bilder, 9 Tabellen, 9 Quellen
Conference paper
English
Luftfahrttechnik , Hubschrauber , Rotor (Drehflügel) , belastetes Bauteil , Flugzeugrumpf , Betriebsverhalten , Flugverhalten , Eigenstabilität , Werkstoffbeanspruchung , Betriebsparameter , Prognose , Rechenmodell , Netzwerkanalyse , Flugregelung , Fluggeschwindigkeit (Luft) , Simulationsmodell , technische Beschreibung , Leistungsbewertung
Prediction of Helicopter Component Loads Using Neural Networks
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