Research on soft sensing of aircraft nose landing gear shimmy damp based on neural network
Research on soft sensing of aircraft nose landing gear shimmy damp based on neural network
- Author(s): Z. Liu 1 ; X. Li 1 ; X. Ding 1 ; Z. Wang 1
- DOI: 10.1049/icp.2021.0418
For access to this article, please select a purchase option:
Buy conference paper PDF
Buy Knowledge Pack
IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.
Thank you
Your recommendation has been sent to your librarian.
- Author(s): Z. Liu 1 ; X. Li 1 ; X. Ding 1 ; Z. Wang 1
-
-
View affiliations
-
Affiliations:
1:
Department of Mechanical & Electronic System , AVIC The First Aircraft Institute , Xi’an, 710089 China
Source:
CSAA/IET International Conference on Aircraft Utility Systems (AUS 2020),
2021
p.
407 – 411
-
Affiliations:
1:
Department of Mechanical & Electronic System , AVIC The First Aircraft Institute , Xi’an, 710089 China
- Conference: CSAA/IET International Conference on Aircraft Utility Systems (AUS 2020)
- DOI: 10.1049/icp.2021.0418
- ISBN: 978-1-83953-419-5
- Location: Online Conference
- Conference date: 18-21 September 2020
- Format: PDF
The mechanism of the nose wheel shimmy is complicated and influenced by many factors, and it shows nonlinear characteristics. The damping characteristics of the nose wheel steering system of a certain aircraft are measured by the damping test. The relationships between the damping characteristics of the nose wheel steering system and the damping aperture diameter, the excitation frequency and the excitation amplitude of the system are analyzed. Rely on the powerful nonlinear mapping ability and generalization function of BP neural network, built up the soft sensing model of BP neural network structure, whose inputs are shimmy damping aperture diameter, excitation frequency and excitation amplitude and output is shimmy damping values. Learned and predicted the model use the neural network. The prediction results proved the feasibility and practicability of this method.
Inspec keywords: backpropagation; aerospace computing; gears; damping; vehicle dynamics; aircraft; steering systems; soft sensors; mechanical engineering computing; neural nets; wheels
Subjects: Computerised instrumentation; Civil and mechanical engineering computing; Vibrations and shock waves (mechanical engineering); Mechanical drives and transmissions; Aerospace industry; Mechanical engineering applications of IT; Neural nets; Aerospace engineering computing; Vehicle mechanics; Mechanical components