Fault diagnosis method for rolling bearings based on minimum entropy deconvolution and autograms

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Journal of Vibration and Shock ›› 2020, Vol. 39 ›› Issue (18) : 118-124.

WANG Xinglong1，ZHENG Jinde1，PAN Haiyang1，TONG Jinyu1，LIU Qingyun1，DING Keqin2

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Abstract

The vibration signal of rolling bearing generally has low signal-to-noise ratio and strong non-Gaussian noise. How to accurately select the demodulation frequency band is still a difficult problem of fault diagnosis of rolling bearing. Autogram is a new optimal band selection method. By calculating the kurtosis of unbiased autocorrelation of the squared envelope of the demodulated signal, the demodulation band and its fault frequency can be effectively detected. But, Autogram is susceptible to noise and the fault feature is not obvious. To overcome this, a new fault diagnosis method for rolling bearing based on the minimum entropy deconvolution (MED) and Autogram is proposed. The proposed method can effectively highlight the fault characteristics and obtain the best band for demodulation. The proposed method is compared with the fast spectral kurtosis and the existing methods by simulation and experimental data of rolling bearing analysis. The results show that the proposed fault diagnosis method for rolling bearing can accurately detect the demodulation frequency band, highlight the fault frequency and improve the effect of fault detection.

Key words

Minimum entropy deconvolution / Autogram / Fast spectral kurtogram / demodulation band

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WANG Xinglong1，ZHENG Jinde1，PAN Haiyang1，TONG Jinyu1，LIU Qingyun1，DING Keqin2. Fault diagnosis method for rolling bearings based on minimum entropy deconvolution and autograms[J]. Journal of Vibration and Shock, 2020, 39(18): 118-124

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