An adaptively fast ensemble empirical mode decomposition method and its applications to rolling element bearing fault diagnosis

Abstract

Ensemble empirical mode decomposition (EEMD) represents a significant improvement over the original empirical mode decomposition (EMD) method for eliminating the mode mixing problem. However, the added white noises generate some tough problems including the high computational cost, the determination of the two critical parameters (the amplitude of the added white noise and the number of ensemble trials), and the contamination of the residue noise in the signal reconstruction. To solve these problems, an adaptively fast EEMD (AFEEMD) method combined with complementary EEMD (CEEMD) is proposed in this paper. In the proposed method, the two critical parameters are respectively fixed as 0.01 times standard deviation of the original signal and two ensemble trials. Instead, the upper frequency limit of the added white noise is the key parameter which needs to be prescribed beforehand. Unlike the original EEMD method, only two high-frequency white noises are added to the signal to be investigated with anti-phase in AFEEMD. Furthermore, an index termed relative root-mean-square error is employed for the adaptive selection of the proper upper frequency limit of the added white noises. Simulation test and vibration signals based fault diagnosis of rolling element bearing under different fault types are utilized to demonstrate the feasibility and effectiveness of the proposed method. The analysis results indicate that the AFEEMD method represents a sound improvement over the original EEMD method, and has strong practicability.