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

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.