Abstract
Rolling bearings are widely used in modern machinery and equipment, and the tough working environment is easy to cause their failure. To solve the problem of extracting fault signals of rolling bearings in a strong noise environment, a method based on Normalized Least Mean Square(NLMS) adaptive filtering and Ensemble Empirical Mode Decomposition(EEMD) noise reduction method is proposed. Firstly, NLMS is used to filter the signal, which is used for primary noise reduction. Then the signal is decomposed into a series of Intrinsic Mode Functions(IMFs) by EEMD, and the kurtosis value, root mean square value and sample entropy value of each IMF are calculated respectively. The appropriate one is selected according to the comprehensive index. Finally, the signal is reconstructed and the Hilbert transform is performed on the reconstructed signal to obtain the envelope spectrum, and the fault characteristic frequency is extracted. Simulation and experimental results show that the method can effectively reduce noise and successfully extract fault features.
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Xi, C., Yang, J., Zhen, D., Liao, X., Hu, W., Gu, F. (2023). Application of Combined Normalized Least Mean Square and Ensemble Empirical Mode Decomposition Denoising Method in Fault Diagnosis of Rolling Bearings. 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_51
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DOI: https://doi.org/10.1007/978-3-030-99075-6_51
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