This content is not included in your SAE MOBILUS subscription, or you are not logged in.
Prediction of Interior Vehicle Noise by Means of NARX Neural Networks
ISSN: 0148-7191, e-ISSN: 2688-3627
Published June 13, 2018 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
Event: 10th International Styrian Noise, Vibration & Harshness Congress: The European Automotive Noise Conference
In recent years, great interest on NVH characteristics of vehicles has been paid by all the big automotive manufacturers. Interior acoustic comfort is now one of the main key factors in vehicle development process, since it contributes to improved product overall quality. Therefore, in automotive industry advanced NVH refinement needs to work in synergy with all research activities. Assessing the level of experienced noise in interior cabin requires particular arrangements for ensuring adequate measurement accuracy (AC system off, closed window, etc.). The use of parameters such as the level of seat vibration, not affected by the acoustic field conditions inside the vehicle, could facilitate experiments in parallel with engine/vehicle calibration activities. These parameters, in addition to information about engine/vehicle speed conditions, may be used to indirectly determine the interior acoustic level in real time, by means of a prediction model based on limited acoustic measurements inside vehicle cabin, previously performed.
Present work describes the development of such a prediction model, properly tuned on the basis of the noise and vibration experimental data acquired inside a passenger car cabin, tested over a track in both stationary and transient operating conditions. More in detail, a Nonlinear Autoregressive with External Input (NARX) Neural Network was implemented for the prediction of the interior noise level over time. The good overall performance as well as the possibility to generalize new data proved the remarkable prediction capabilities of the trained network in a real-world forecasting scenario.
|Technical Paper||Blower Motor Whining Noise - A Case Study|
|Technical Paper||Sound Quality Evaluation of Passenger Vehicle Interior Noise|
CitationSiano, D., Panza, M., and Badan, J., "Prediction of Interior Vehicle Noise by Means of NARX Neural Networks," SAE Technical Paper 2018-01-1538, 2018, https://doi.org/10.4271/2018-01-1538.
Data Sets - Support Documents
|Unnamed Dataset 1|
|Unnamed Dataset 2|
- Siano , D. , Prati , M.V. , Costagliola , M.A. , and Panza , M.A. Evaluation of Noise Level inside Cab of a Bi-Fuel Passenger Vehicle WSEAS Transactions on Applied and Theoretical Mechanics 2015 220 226
- Siano , D. , Viscardi , M. , and Panza , M.A. Recent Advances in Fluid Mechanics and Thermal Engineering 50 57
- Siano , D. Viscardi , M. , and Panza , M.A. Automotive Materials: An Experimental Investigation of an Engine Bay Acoustic Performances Energy Procedia 101 598 605 2016 1876-6102 10.1016/j.egypro.2016.11.076
- Wang , X. Vehicle Noise and Vibration Refinement Woodhead Publishing 2010
- Brogna , G. , Antoni , J. , Totaro , N. , Gagliardini , L. , and Sauvage , O. Modelling Vehicles NVH Performances: A Probabilistic Approach INTER-NOISE and NOISE-CON Congress and Conference Proceedings 2017 1400 1411
- Li , Q. , Qiao , F. , Yu , L. , and Shi , J. Journal of the Air & Waste Management Association 2017
- Costagliola , M.A. , Prati , M.V. , Mariani , A. , Unich , A. , and Morrone , B. Gaseous and Particulate Exhaust Emissions of Hybrid and Conventional Cars over Legislative and Real Driving Cycles Energy and Power Engineering 07 05 181 192 2015
- Rojas , R. Neural Networks: A Systematic Introduction Springer Science & Business Media 2013
- Marques , F.D. , Rodrigues de Souza , L.d.F. , Rebolho , D.C. , Caporali , A.S. et al. Application of Time-Delay Neural and Recurrent Neural Networks for the Identification of a Hingeless Helicopter Blade Flapping and Torsion Motions Journal of the Brazilian Society of Mechanical Sciences and Engineering 27 2 2005
- Levin , R.I. and Lieven , N.A. Dynamic Finite Element Model Updating Using Neural Networks Journal of Sound and Vibration 210 5 593 607 1998
- MathWorks® 2017
- Xie , H. , Tang , H. , and Liao , Y. H. Time Series Prediction Based on NARX Neural Networks: An Advanced Approach Machine Learning and Cybernetics, 2009 International Conference on 2009 1275 1279
- Diaconescu , E. The Use of NARX Neural Networks to Predict Chaotic Time Series Wseas Transactions on Computer Research 3 3 182 191 2008
- Karsoliya , S. Approximating Number of Hidden layer neurons in Multiple Hidden Layer BPNN Architecture International Journal of Engineering Trends and Technology 3 6 2012
- Roweis , S.