Local damage detection in rolling element bearings based on a single ensemble empirical mode decomposition

Abstract

A Single Ensemble Empirical Mode Decomposition (SEEMD) is proposed to locate the damage in rolling element bearings. The SEEMD method eliminates the need for adding or subtracting noise repeatedly to process signals, unlike other techniques that rely on multiple ensembles. The SEEMD requires a single sifting process of a modified raw signal to reduce the computation time significantly. The other advantage of the SEEMD method is its success in dealing with non-Gaussian or non-stationary perturbing signals. In SEEMD, a fractional Gaussian noise (FGN) is initially added to the raw signal to emphasize the signal's high frequencies. Then, a convoluted white Gaussian noise is multiplied on the resulting signal, changing its spectral content, which helps in extracting the weak periodic signal. Finally, the obtained signal is decomposed using a single sifting process. The proposed methodology is applied to the raw signals obtained from the mining industry. These signals are difficult to analyze since cyclic impulsive components are obscured by noise and other interference. Based on the results, the proposed method can effectively detect the fault where the signal of interest (SOI) has been extracted with good quality.