Abstract
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.
Highlights
The ICA cannot identify the actual number of source signals but we can ensure that whitening can be done for static signal processing as linear transformation between the signals into independent components measured by some factor of independence
Our work is a useful tool for the purpose of defining the process of signal noise extraction and noise power spectral density measurement from the vibrating rails
We have developed analytical expressions to find the performance of the estimate and we have corroborated this study applying Independent component analysis based on signal separation and information detection for the estimation of the power spectral density for the spectrum of the tram data
Summary
The advantage of the proposed wavelet in this work is that harmonic frequency can be controlled by the correct selection of F c , and the scale parameter a This provides extra flexibility to obtain a good time–frequency resolutionPrior to this technique, noise frequency translation for spectrum representation was increased by coherent gain for noise spectrum identification [3] and a correction factor was applied using Blackman Harris [4] algorithm to identify more number of signal sub segments in the noise signal. It is imperative to define certain rule based features including Independent Component Analysis and likelihood identification that is good in the purpose of solving the problem of signal distinction and frequency components generated as a result of signal vibration in the tram tracks. A sudden increase in vibration levels can be an indication of a significant weakening of the structure rail that can sustain the prevailing shock and vibration caused by discontinuities caused by the impact rails
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