Abstract
In this work, a novel bearing fault diagnosis method based on EEMD and adaptive redundant lifting scheme packet is proposed. Firstly, EEMD method is used to decompose rolling bearing signals of different fault types, and the correlation coefficient criterion method is carried out in order to screen effective IMF components and reconstruct them. Then, the adaptive redundant lifting scheme packet method is used to denoise the reconstructed signal, and the energy characteristics of different fault signals are extracted. Finally, the bearing fault diagnosis system is constructed by BP neural network diagnosis. The results show that the diagnostic method proposed in this paper has better diagnosis efficiency and precision than the traditional wavelet packet.
Highlights
Rolling bearing is one of the most widely used components and parts in rotating machinery
In response to the above problems, this paper is on the basis of literature [4], based on the EEMD (Ensemble Empirical Mode Decomposition, EEMD) and adaptive redundant lifting scheme packet bearing fault diagnosis method, through non-sampling operation, keeping the signal length of each frequency band consistent with the initial signal length, effectively retaining the impact component information in the bearing signal, make the information redundant, achieve the purpose of accurately extracting the frequency of rolling bearing faults, and successfully realize the diagnosis of such faults
In order to test the effectiveness of the diagnosis method proposed in this paper, the traditional wavelet packet and redundant lifting scheme packet are respectively used for denoising pre-treatment, and the same BP neural network is used for training
Summary
Rolling bearing is one of the most widely used components and parts in rotating machinery. Due to the sampling operation of wavelet packet transform, the length of each frequency band during signal decomposition is only 1/2 of the signal length before decomposition. In response to the above problems, this paper is on the basis of literature [4], based on the EEMD (Ensemble Empirical Mode Decomposition, EEMD) and adaptive redundant lifting scheme packet bearing fault diagnosis method, through non-sampling operation, keeping the signal length of each frequency band consistent with the initial signal length, effectively retaining the impact component information in the bearing signal, make the information redundant, achieve the purpose of accurately extracting the frequency of rolling bearing faults, and successfully realize the diagnosis of such faults.
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