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

被引：3

作者：

Li, L ^{[1
]}

Wang, FY ^{[1
]}

Zhou, QZ ^{[1
]}

Shan, GL ^{[1
]}

机构：

[1] Univ Arizona, Program Adv Res Complex Syst, Tucson, AZ 85721 USA

来源：

IEEE IV2003: INTELLIGENT VEHICLES SYMPOSIUM, PROCEEDINGS | 2003年

关键词：

D O I：

10.1109/IVS.2003.1212887

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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 effectives of this new fault detector.

引用

页码：80 / 84

页数：5

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