Early detection of gear faults in variable load and local defect size using ensemble empirical mode decomposition (EEMD)

Abstract

In stationary condition, when a local gear fault occurs, both amplitude and phase of the tooth meshing vibration are modulated. If the rotating speed of the shaft is invariable, the gear-fault-induced modulation phenomenon manifest as frequency sidebands equally spaced around the meshing frequency and its harmonics in vibration spectra. However, under variable load and rotating speed of the shaft, the meshing frequency and its harmonics and the sidebands vary with time and hence the vibration signal becomes non-stationary. Using Fourier transform doesn’t allow detecting the variation of the rotating machine and its harmonics which reflect the gear fault. In this study, we propose to use the ensemble empirical decomposition (EEMD) to decompose signals generated by the variation of load and the size of the defect. This method is particularly suitable for processing non stationary signals. By using EEMD the signal can be decomposed into a number of IMFs which are mono component, we use also the spectrum and spectrogram of each IMF to show and calculate the frequency defect.