Early fault diagnosis of bearings based on multilayer hybrid de-noising

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Journal of Vibration and Shock ›› 2018, Vol. 37 ›› Issue (8) : 22-27.

LV Jingxiang, YU Jianbo

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Abstract

In order to extract fault features embedded in weak signals consisting of much noise, a new method named local mean decompositionmultilayer hybrid de-noising (LMD-MHD) was proposed. A series of product functions (PFs) were obtained after LMD. Multiple criteria decision was proposed to select the effective PF components reasonably, which combined quantitative ability of each index at different fault stages. Then, the wavelet threshold de-noising was used as the pre-filter process. The order of effective ranks was determined by the number of main frequency in the Fast Fourier transformation result of the signal. Experimental results on the actual bearing vibration signals demonstrate that this method can effectively remove interference of noise and extract the faint fault features. Thus, the proposed method can be used to improve the accuracy of bearing fault diagnosis.

Key words

Faint fault diagnosis / Local mean decomposition / De-noising / Singular value decomposition

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LVJingxiang, YU Jianbo. Early fault diagnosis of bearings based on multilayer hybrid de-noising[J]. Journal of Vibration and Shock, 2018, 37(8): 22-27

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