A novel wind turbine bearing fault diagnosis method based on Integral Extension LMD. (October 2015)

• Record Type: Journal Article
• Authors: Liu, W.Y.; Gao, Q.W.; Ye, G.; Ma, R.; Lu, X.N.; Han, J.G.
• Is Part Of: Measurement. Volume 74(2015:Oct.)
• Page Start: 70
• Page End: 77
• Publication Date: 2015-10
• Subjects: Integral Extension Local Mean Decomposition (IELMD) -- Wind turbine -- End effect -- LMD -- Fault diagnosis; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2015.06.005
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: A novel ball bearing fault diagnosis method based on Integral Extension Local Mean Decomposition (IELMD) is proposed. The integral extension can suppress the end effects of LMD. Both the left and right side extreme points are extended. The ball bearing fault diagnosis experiment improved the efficiency and validity of the new IELMD method. Abstract: This paper proposed a novel wind turbine ball bearing fault diagnosis method based on Integral Extension Local Mean Decomposition (IELMD). Wind turbine vibration signal has the characteristic of non-Gaussian and non-stationary. Some typical time–frequency analysis methods cannot achieve ideal effects. A new method named LMD can deal with non-stationary signals and can extract obvious features. The IELMD method is proposed based on the integral local waveform matching of the right and left side of the original signal, in order to suppress the end effect of LMD method itself. Firstly, all the extreme points are scanned out, in which the left three points and right three points are specially marked. Secondly, two characteristic waveforms and two matching waveforms are established at both the left and right side. Finally, both the left and right matching waveforms are extended to finish the LMD process. The wind turbine bearing fault diagnosis experimental can improve the efficiency and validity of the novel IELMD method.