NDLI: Aircraft cabin noise minimization via neural network inverse model

Content Provider	IEEE Xplore Digital Library
Author	Xiao Hu Clark, G. Travis, M. Vian, J. Wunsch, D.C.
Copyright Year	2005
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Missouri Univ., Rolla, MO, USA (Xiao Hu)
Abstract	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.
Starting Page	2341
Ending Page	2346
File Size	2772902
Page Count	6
File Format	PDF
ISBN	0780390482
DOI	10.1109/IJCNN.2005.1556267
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-07-31
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Aircraft Neural networks Inverse problems Testing Engines Artificial neural networks Integrated circuit noise Noise generators Integrated circuit modeling Airplanes
Content Type	Text
Resource Type	Article

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