NDLI: Bispectral feature analysis and diagnosis for bearing failure of direct-drive wind turbine

Content Provider	IEEE Xplore Digital Library
Author	Liu Yibing Zhou Yanbing Xin Weidong Gao Qingfeng He Ying
Copyright Year	2011
Description	Author affiliation: School of Energy, Power and mechanical Engineering, North China Electric Power University, Beijing 102206, China (Liu Yibing; Zhou Yanbing; Xin Weidong; Gao Qingfeng; He Ying)
Abstract	Vibration measurements on a large direct-drive wind turbine are carried out to detect the fault of the rolling element bearing on the rear part of the main shaft. The main purpose of this paper is to find an appropriate method for weak fault feature extraction and fault recognition from vibration signals under strong interference. We propose to use higher order statistics characteristics of vibration signals for differentiating normal condition from fault condition. Firstly the non-Gaussian intensity in different area in vibration signal bispectrum are extracted as the feature values, these multi-dimensional features are compressed by means of principal component analysis (PCA) to obtain some lower dimensional principal component features with better discrimination for different running condition. Analysis results show that the proposed method of feature extraction with the non-Gaussian intensity characteristic of the bispectrum is very sensitive to differentiate the normal running condition from failure ones and very clear to identify the bearing fault of wind turbine.
Starting Page	3169
Ending Page	3174
File Size	1399060
Page Count	6
File Format	PDF
ISBN	9781457706776
ISSN	21612927
e-ISBN	9789881725592
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-22
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Chinese Assoc of Automati
Subject Keyword	Vibrations Direct-Drive Wind Turbine Bearing Fault Vibration measurement Feature extraction Wind turbines Electronic mail Bispectral Analysis Principal component analysis Higher order statistics PCA
Content Type	Text
Resource Type	Article

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