CALCULATION OF THE FREQUENCY BANDS OF BEARING VIBRATION FOR THE TRACTION GEARBOXES OF ELECTRIC TRAINS BY THE MULTIRESOLUTION ANALYSIS

Abstract

The paper deals with the property of discrete wavelet transform based on the multiresolution analysis feature to identify different types of the gear and bearing vibration. The direct analysis of the vibration time series by the use of conventional statistical measures, such as mean, root mean square, standard deviation, is not always useful due to the complexity of the signal. It was proposed to choose the best mother wavelet which is able to identify the transients in vibration signal according to the calculated minimum value of Shannon entropy, which quantifies the level of uncertainty of a given vibration time series. The main idea is that when a bearing is healthy, it will produce low amplitude random vibration with a uniform-like probability mass function and as the fault occurs and progresses some probability mass function component will be prevalent with a higher probability of occurrence. The chosen Daubechies wavelet of the 4-th order has decomposed the acquired vibration signal of the traction gearbox of electric train into approximated and detailed coefficients on four decomposition scales with further reconstruction of the signals on the appropriate scales according to the above-mentioned coefficients. The autocorrelation was applied for the detection of deterministic and random components in the reconstructed signals through evaluation of the impulse periodicities of the reconstructed signals according to the detailed coefficients at all scales and has taken the sinusoidal shape for the reconstructed signals according to the approximated coefficients. It was established that a deterministic vibration component dominates and there are no bearing damage features in the reconstructed signals according to the approximated coefficients due two strong gearmesh harmonics. The presence of impulse periodicity on the reconstructed signals according to the detailed coefficients at the second decomposition scale is possible to monitor due to the correlogram, which can be explained by the periodic contact of the damaged element with other elements during their rotation in bearing. The kurtosis is applied as a reliable tool for the frequency band selection where the bearing vibration has the strongest excitation