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.
X. Hu et al., "Aircraft Cabin Noise Minimization Via Neural Network Inverse Model," Proceedings of the IEEE International Joint Conference on Neural Networks, 2005, Institute of Electrical and Electronics Engineers (IEEE), Jan 2005.
The definitive version is available at http://dx.doi.org/10.1109/IJCNN.2005.1556267
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
Article - Conference proceedings
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