This paper describes research to investigate an artificial neural network (ANN) approach to minimize aircraft cabin noise in flight. The ANN approach is shown to be able to accurately model the non-linear relationships between engine unbalance, airframe vibration, and cabin noise to overcome limitations associated with traditional linear influence coefficient methods. ANN system inverse models are developed using engine test-stand vibration data and on-airplane vibration and noise data supplemented with influence coefficient empirical data. The inverse models are able to determine balance solutions that satisfy cabin noise specifications. The accuracy of the ANN model with respect to the real system is determined by the quantity and quality of test stand and operational aircraft data. This data-driven approach is particularly appealing for implementation on future systems that include continuous monitoring processes able to capture data while in operation.

Meeting Name

IEEE International Joint Conference on Neural Networks, 2005


Electrical and Computer Engineering

Keywords and Phrases

Aerospace Engineering; Aircraft Cabin Noise Minimization; Aircraft Control; Artificial Intelligence; Artificial Neural Network; Engine Test-Stand Vibration Data; Influence Coefficient Empirical Data; Interference Suppression; Inverse Problems; Jet Engines; Neural Nets; Neural Network Inverse Model; On-Airplane Vibration; Vibration Control

Document Type

Article - Conference proceedings

Document Version

Final Version

File Type





© 2005 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 Jan 2005