Local damage detection in rolling element bearings based on a Single Ensemble Empirical Mode Decomposition

A Single Ensemble Empirical Mode Decomposition (SEEMD) is proposed for locating the damage in rolling element bearings. The SEEMD does not require a number of ensembles from the addition or subtraction of noise every time while processing the signals. The SEEMD requires just 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, initially, a fractional Gaussian noise (FGN) is added to the raw signal to emphasize on high frequencies of the signal. Then, a convoluted white Gaussian noise is multiplied to the resulting signal which changes the spectral content of the signal which helps in extraction of the weak periodic signal. Finally, the obtained signal is decomposed by 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.

Non-parametric Ensemble Empirical Mode Decomposition for extracting weak features to identify bearing defects

A non-parametric complementary ensemble empirical mode decomposition (NPCEEMD) is proposed for identifying bearing defects using weak features. NPCEEMD is non-parametric because, unlike existing decomposition methods such as ensemble empirical mode decomposition, it does not require defining the ideal SNR of noise and the number of ensembles, every time while processing the signals. The simulation results show that mode mixing in NPCEEMD is less than the existing decomposition methods. After conducting in-depth simulation analysis, the proposed method is applied to experimental data. The proposed NPCEEMD method works in following steps. First raw signal is obtained. Second, the obtained signal is decomposed. Then, the mutual information (MI) of the raw signal with NPCEEMD-generated IMFs is computed. Further IMFs with MI above 0.1 are selected and combined to form a resulting signal. Finally, envelope spectrum of resulting signal is computed to confirm the presence of defect.