An adaptive optimization EEMD method and its application in bearing fault detection

Abstract

Abstract Aiming at the optimization of two important parameters (white noise amplitude coefficient and set average number) in the set empirical mode decomposition (EEMD), an adaptive EEMD parameter optimization method is proposed. First of all, this paper extracts the corresponding amplitude of the high-frequency component of the signal through the energy value of the first eigenmode function, uses the relative mean square error to determine the corresponding amplitude of the low-frequency component of the signal, and establishes the optimal amplitude evaluation criteria based on the corresponding amplitude of the two; At the same time, in order to improve the calculation efficiency and reduce the influence of white noise, the energy value of the first modal component is used to determine the optimal average number of sets; Then, the effectiveness of the method in this paper is verified by simulation experiments; Finally, this method is applied to the extraction of bearing inner ring fault vibration signal. The results show that compared with the traditional EEMD method, this method can adaptively determine the noise amplitude and the set average number, and can more effectively identify the periodic fault components of the vibration signal.