Simulated fault data from a mathematical model of a damaged rotor system is used to develop a neural network based approach for rotor system damage detection. The mathematical model of the damaged rotor is a comprehensive rotorcraft aeroelastic analysis based on a finite element approach in space and time. Selected helicopter rotor faults are simulated through changes in inertial, damping and stiffness properties of the damaged blade. A feedforward neural network with backpropagation learning is trained using both 'ideal' and 'noisy' simulated data. Testing of the trained neural network shows that it can detect and identify damage in the rotor system from simulated blade response and vibratory hub loads data.
Detection of helicopter rotor system simulated faults using neural networks
Erkennung von simulierten Fehlern eines Hubschrauberrotorsystems mit Hilfe neuronaler Netze
1996
18 Seiten, 22 Bilder, 6 Tabellen, 20 Quellen
Aufsatz (Konferenz)
Englisch
Detection of Helicopter Rotor System Simulated Faults Using Neural Networks
Online Contents | 1997
|Detection of helicopter rotor system simulated faults using neural networks
Tema Archiv | 1997
|Detection of Helicopter Rotor System Simulated Faults Using Neural Networks
British Library Conference Proceedings | 1996
|Helicopter Rotor System Fault Detection Using Physics-Based Model and Neural Networks
Online Contents | 1998
|