Abstract
In view of the problem that the fault signal of the rolling bearing is weak and the fault feature is difficult to extract in the strong noise environment, a method based on minimum entropy deconvolution (MED) and local mean deconvolution (LMD) is proposed to extract the weak fault features of the rolling bearing. Through the analysis of the simulation signal, we find that LMD has many limitations for the feature extraction of weak signals under strong background noise. In order to eliminate the noise interference and extract the characteristics of the weak fault, MED is employed as the pre-filter to remove noise. This method is applied to the weak fault feature extraction of rolling bearings; that is, using MED to reduce the noise of the wind turbine gearbox test bench under strong background noise, and then using the LMD method to decompose the denoised signals into several product functions (PFs), and finally analyzing the PF components that have strong correlation by a cyclic autocorrelation function. The finding is that the failure of the wind power gearbox is generated from the micro-bending of the high-speed shaft and the pitting of the #10 bearing outer race at the output end of the high-speed shaft. This method is compared with LMD, which shows the effectiveness of this method. This paper provides a new method for the extraction of multiple faults and weak features in strong background noise.
Highlights
The wind turbine is an important piece of equipment in modern agricultural production
Each physical functions (PFs) component is obtained by multiplying an envelope signal and a purely frequency modulated (FM) signal
As a strong noise reduction method, minimum entropy deconvolution (MED) uses the maximum kurtosis as the termination condition of the algorithm, which can highlight the strong impact component of the original signal and weaken the effect of noise on local mean deconvolution (LMD)
Summary
The wind turbine is an important piece of equipment in modern agricultural production. As a strong noise reduction method, MED uses the maximum kurtosis as the termination condition of the algorithm, which can highlight the strong impact component of the original signal and weaken the effect of noise on LMD In this approach, the MED-based denoising method is used to reduce the effect of the measurement’s noise signal. For the poor performance of weak signal feature extraction of rolling bearing by LMD in a strong noise environment, a method of combining of MED-based denoising and LMD has been proposed. The MED method is first employed as the pretreatment to denoise the weak signal, and the purified signal obtained through MED denoising is decomposed by LMD into PFs. the PFs corresponding to the faulty feature signal are analyzed by cyclic autocorrelation function demodulation and the fault features are extracted
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