In this paper, the empirical mode decomposition as a signal processing method has been studied to overcome one of its shortcomings. In the previous studies, some improvements have been made on the empirical mode decomposition and it has been applied for condition monitoring of mechanical systems. These improvements include elimination of mode mixing and restraining of end effect in empirical mode decomposition method. In this research, to increase the accuracy of empirical mode decomposition, a new local mean has been proposed in the sifting process. Through the proposed local mean, the overshoot and undershoot problems in defining the local mean of common algorithm are alleviated. Meanwhile, it is capable to separate the components with close frequencies. Through the analysis of simulated signals via the new algorithm, it is shown that the accuracy is improved. Finally, empirical mode decomposition-based fault diagnosis approach has been applied to a vibration signal obtained from a faulty gearbox. The results show that the proposed method can resolve the effects of damage in vibration signals better than the common empirical mode decomposition method and helps for the isolation and localization of the fault.
A modified empirical mode decomposition method for multiperiod time-series detrending and the application in full-waveform induced polarization data