Automatic tire pressure fault monitor using wavelet-based probability density estimation

Author

Li, Li ; Wang, Fei-Yue ; Zhou, Qunzhi ; Shan, Guoling

Author_Institution

Program for Adv. Res. in Complex Syst., Arizona Univ., Tucson, AZ, USA

fYear

2003

Firstpage

80

Lastpage

84

Abstract

This paper is devoted to the problem of tire pressure monitoring and tire fault detection. Based on wavelet package transformation, the density of the tire´s response vibration caused by the stochastic ground excitation is analyzed. Then using RBF neural networks to learn and classify the different types of vibration response, an automatic abnormal tire pressure detector is built. Theoretical analysis and simulation results show the I effectives of this new fault detector.

Keywords

computerised monitoring; fault diagnosis; pressure sensors; probability; radial basis function networks; road vehicles; tyres; vibrations; wavelet transforms; RBF neural networks; automatic abnormal tire pressure detector; automatic tire pressure fault monitor; stochastic ground excitation; tire fault detection; tire response vibration density; wavelet based probability density estimation; wavelet package transformation; Automation; Computerized monitoring; Fault detection; Friction; Intelligent systems; Laboratories; Neural networks; Stochastic processes; Tires; Wavelet analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Vehicles Symposium, 2003. Proceedings. IEEE

Print_ISBN

0-7803-7848-2

Type

conf

DOI

10.1109/IVS.2003.1212887

Filename

1212887