Abstract
Rolling bearing is a kind of easily damaged mechanical equipment. The quality of rolling bearing is related to the normal operation of the equipment. Because the resonance demodulation method is susceptible to noise interference, and the band-pass filter parameters are largely dependent on personal experience selection. This paper proposes an analysis method based on the combination of Ensemble Empirical Mode Decomposition (EEMD) and the selection criterion of kurtosis-cross-correlation coefficient. Firstly, the vibration signal is decomposed by EEMD to get intrinsic mode functions (IMFs); Secondly, since the decomposed IMF components will produce mode aliasing, two criteria of cross-correlation coefficient and kurtosis are introduced to extract effective IMF components for signal reconstruction; Finally, the reconstructed signal is subjected to Hilbert transform and envelope analysis. Compared with the resonance demodulation analysis method, the EEMD decomposition method is selected to replace the band-pass filter to reduce the noise of the signal, which enhances signal to noise ratio and makes the fault characteristics more obvious. The experimental signal analysis results of rolling bearing faults show that a refinement of methodology presented in this article can effectively extract the fault characteristics of rolling bearing, and has more advantages than traditional envelope analysis methods.
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Zhang, W., Tian, X., Liu, G., Liu, H. (2023). A Fault Diagnosis Method for Rolling Bearings Based on Improved EEMD and Resonance Demodulation Analysis. In: Zhang, H., Feng, G., Wang, H., Gu, F., Sinha, J.K. (eds) Proceedings of IncoME-VI and TEPEN 2021. Mechanisms and Machine Science, vol 117. Springer, Cham. https://doi.org/10.1007/978-3-030-99075-6_54
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