Rolling Bearing Fault Diagnosis Method Based on MOMEDA and IEWT

Zhi-Wu Shang ； Geng Rui ； Li Cheng ； Liu Xia ； Mao-Sheng Gao ； Jin-Tian Yun

《International Journal of Information and Management Sciences》 29卷4期 (2018/12) Pp. 345-363

https://doi.org/10.6186/IJIMS.201812_29(4).0001

摘要

Aiming at the problem of early weak fault of non-stationary vibration signal in the background of complicated and heavy noises, a rolling bearing fault diagnosis technology based on multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) and improved empirical wavelet transform (IEWT) is proposed. First, the acquired signal is analyzed by multipoint kurtosis (Mkurt) to obtain the approximate period of the signal and according to the obtained period, a reasonable period interval is determined. Using denoised method based on MOMEDA is to extract the periodic impulse of the original signal. Second, by using IEWT method, the denoised signal can be decomposed into number of intrinsic mode functions (IMFs). Finally, the IMFs are used the maximum kurtosis criterion for screening sensitive component, and then using Hilbert envelope spectrum analysis identifies the type of fault to achieve fault diagnosis. The experimental result shows that the proposed method can effectively improve the performance of fault diagnosis of rolling bearing.

關鍵字

Rolling bearing ； MOMEDA ； EWT ； non-stationary vibration signal ； envelope spectrum

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