Local Damage Detection Method Based on Distribution Distances Applied to Time-Frequency Map of Vibration Signal.

Problem of the local damage detection is a crucial task in modern condition monitoring. However, most of the classical methods rely on being tested in laboratories in the isolated test stands. In case of the real world data, there are outer sources of the noise (for instance nearby machines). Such noise can affect shape of the signal and increase the level of difficulty in finding information about the fault. Thus, one can define problem of the fault detection as a finding cyclic impulses in the signal. In this paper, the authors propose a procedure for the local damage detection based on the time-frequency decomposition and distance between distributions. Local damage in bearings/gearbox provides specific response in the vibration signal. It can be further investigated via time-frequency decomposition where one can track energy distribution change in time. Applying measure of distances between distributions to absolute value of short time Fourier transform (STFT) matrix of the vibration signal, one can find deviation of subsequent samples from one used as a referential sample. Furthermore, thresholding of the absolute value of the STFT matrix, based on the log-variance of this matrix with respect to frequency allows for the enhancement of the results. In the extension of the proposed methods, we also use normalization of the spectrogram to omit additional step of the log-variance thresholding. Knowledge, which one needs to set appropriate threshold, can set this step to be difficult in the industrial application and thus, normalization of the spectrogram is proposed. Analyzed real signals were acquired from the belt conveyor driving unit in the mining facility. [ABSTRACT FROM AUTHOR]