WSEAS Transactions on Systems and Control

Print ISSN: 1991-8763
E-ISSN: 2224-2856

Volume 12, 2017

Notice: As of 2014 and for the forthcoming years, the publication frequency/periodicity of WSEAS Journals is adapted to the 'continuously updated' model. What this means is that instead of being separated into issues, new papers will be added on a continuous basis, allowing a more regular flow and shorter publication times. The papers will appear in reverse order, therefore the most recent one will be on top.

Volume 12, 2017

Identification and Prediction of Road Features and their Contribution on Tire Road Noise

AUTHORS: Johannes Masino, Benjamin Wohnhas, Michael Frey, Frank Gauterin

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ABSTRACT: Traffic noise has large consequences on the appreciation of the living quality close to roads and is considered as a health problem today. It leads to speech interference, sleep disturbances, and general annoyance. The major contributor to traffic noise is tire/road noise. Many studies show the influence of different road types or vehicles on tire/road noise or on the noise inside the vehicle. This study focuses on the contribution of the overdrive of different road features on the tire/road noise for various velocities. Experimental measurements based on the ISO-13325 Coast-By Method are performed to determine the relative Sound Pressure Level of road features compared to a normal Asphalt road in good condition. The results show, that road features cause at least 4 dB higher Sound Pressure Level than the reference Asphalt road for the investigated velocities, except for manhole cover at 8:3 m=s. Therefore, road features and damages have a significant contribution to tire/road noise and on human health. To identify and predict such road features, we present a method based on vehicle sensors and data mining methods. The sensors are an inertial sensor placed at the centre of gravity of the vehicle and a sound pressure sensor in the tire cavity. The sensors combined with the data analysis method represent a strong system to comprehensively and automatically identify and predict road features, the road infrastructure condition and subsequently road segments with a high value in tire/road noise. With the output of the presented method maintenance and repairs can be done efficiently, which contributes to lower tire/road noise and less disturbance of residents.

KEYWORDS: Tire/Road Noise, Vehicle Vibration, Tire Vibration, Road Features, Data Mining, Vehicle Sensor, Experimental Study


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WSEAS Transactions on Systems and Control, ISSN / E-ISSN: 1991-8763 / 2224-2856, Volume 12, 2017, Art. #21, pp. 201-212

Copyright © 2017 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution License 4.0

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