Empirical Mode Decomposition of Vibration Signal for Detection of Local Disturbances in Planetary Gearbox Used in Heavy Machinery System

Abstract

In rotating machinery, the detection of local damage is one of the most important issues. This kind of change of technical condition produce local disturbance according to temporal (local) change of stiffness of kinematic pair (tooth-tooth contact, rolling element-outer/inner race etc). In many practical, i.e. industrial cases, vibration signature of such change is weak in sense of produced energy, so consequently, completely masked by other vibration sources in machine. The general concept of signal processing for local damage detection is to use so called signal enhancement, i.e. a kind of tool that may improve signal to noise ratio. One may find many approaches used in the literature. Most of them use signal filtering (classical, adaptive and optimal filters), decomposition (wavelets) or extraction (blind source separation). Empirical Mode Decomposition (EMD) is one of such techniques that can be used with signal decomposition problem. In this paper, EMD will be used for vibration signal decomposition in order to extract information about local perturbation of arm (carrier) in planetary gearbox used in heavy mining machine, i.e. bucket wheel excavator. As a result of application of EMD, one may obtain several time series with different properties of sub-signal. Due to predefined task, namely local disturbance detection, several criteria have been investigated in order to select the most informative empirical mode. First criterion was kurtosis calculated for every mode with very simple decision rule (max kurtosis is the best). It was found that such approach is not optimal due to some random impulses that are not related to damage. To improve results, it is proposed to combine envelope spectrum and kurtosis. If envelope spectrum contains family of components related to arm (carrier) shaft frequency and signal is spiky (kurtosis is high) result of EMD for given mode is optimal in sense of carried information. However, in this approach decision was made based on visual inspection of the envelope spectra of each mode, which is non-effective way. Finally two parameters have been proposed: 1) Pearson correlation coefficient of an empirical mode and the empirically determined local mean of original signal; 2) a relative power of an empirical mode.