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Many vibration signals of tram rails due to tram movement are non-stationary and have highly complex time-frequency characteristics. The vibration signal of a rotating wheel involves condition monitoring and fault diagnosis. Many signal analysis methods are able to extract useful information from vibration data. In this paper, we were able to correlate non-linear independent signal acquired using acceleromets at different spots across the city and extract tram rail vibration noise and model the effect of signal noise to identify the frequency characteristics of the rail by characterizing the spectral content of the noise signal using parametric distribution and then by applying non parametric filters to characterize the signal power spectral density using Wavelet Transform (WT) and Parseval’s theorem. The fault can be detected from a given level of resolution. For this purpose, Parseval’s theorem is used as an evaluation criterion to select the optimal level. Associated to envelope analysis, it allows clear visualization of fault frequencies. on the inner rail of the railway line. The time-frequency contour map can easily show the power distribution of signal in time and frequency domain. Moreover, it is a good way to identify the rail track faults involving a breakdown change. The simulative results show that time-frequency contour map have the capabilities to identify the difference of those faults of vibration monitoring. In conclusion, the faults along the rail track can be classified by time-frequency contour map for frequency decomposition. We hereby decompose the high frequency detail of the signal without decomposition after wavelet transform, so as to improve the frequency resolution.

A Case Study of Natural Frequency of the Tram Rail Due to Vibration Using Wavelets