Performance Improvement of Ensemble Empirical Mode Decomposition for Roller Bearings Damage Detection

Ensemble empirical mode decomposition (EEMD) is a noise assisted method widely used for roller bearing damage detection. However, to successfully handle this technique still remains a great challenge: identification of two effective parameters (the amplitude of added noise and the number of ensemble trials), which affect the performances of the EEMD. Although a number of algorithms or values have been proposed, there is no robust guide to select optimal amplitude and the ensemble trial number yet, especially for early damage detection. In this study, a reliable method is proposed to determine the suitable amplitude and the proper number of trials is investigated as well. It is shown that the proposed method (performance improved EEMD) achieves higher damage detection success rate and creates larger Margin than the original algorithm. It leads to a substantially low trial numbers required to achieve perfect labelling of samples; in turn this fact leads to considerably less computational cost. The number of real vibration signals is analysed to verify effectiveness and robustness of the proposed method in discriminating and separating the faulty conditions.