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

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

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 diffe...

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Bibliographische Detailangaben
Hauptverfasser: Li Li, Fei-Yue Wang, Qunzhi Zhou, Guoling Shan
Format: Tagungsbericht
Sprache:eng
Schlagworte:
Automation
Computerized monitoring
Fault detection
Friction
Intelligent systems
Laboratories
Neural networks
Stochastic processes
Tires
Wavelet analysis
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Beschreibung
Zusammenfassung: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.
DOI:10.1109/IVS.2003.1212887