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Entropy 2017, 19(6), 277;

Weak Fault Diagnosis of Wind Turbine Gearboxes Based on MED-LMD

The School of Mechanical and Power Engineering, North University of China, Xueyuan Road, Taiyuan 030051, China
Authors to whom correspondence should be addressed.
Academic Editor: Carlo Cattani
Received: 12 April 2017 / Revised: 2 June 2017 / Accepted: 7 June 2017 / Published: 15 June 2017
(This article belongs to the Special Issue Wavelets, Fractals and Information Theory III)
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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. View Full-Text
Keywords: minimum entropy deconvolution; local mean deconvolution; weak fault; multi-fault; cyclic autocorrelation function minimum entropy deconvolution; local mean deconvolution; weak fault; multi-fault; cyclic autocorrelation function

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Wang, Z.; Wang, J.; Kou, Y.; Zhang, J.; Ning, S.; Zhao, Z. Weak Fault Diagnosis of Wind Turbine Gearboxes Based on MED-LMD. Entropy 2017, 19, 277.

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