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

机译：直接驱动风力涡轮机轴承故障的双光谱特征分析及诊断

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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.
机译：在大型直接驱动风力涡轮机上进行振动测量，以检测主轴后部滚动元件的故障。本文的主要目的是在强烈干扰下找到一种适用于弱故障特征提取和故障识别的适当方法。我们建议使用振动信号的更高阶统计特征来区分常规条件的故障状态。首先，振动信号BISPectrum中的不同区域中的非高斯强度被提取为特征值，这些多维特征通过主成分分析（PCA）来压缩，以获得一些具有更好识别的一些较低的尺寸主体组件特征，对不同的运行更好地辨别健康）状况。分析结果表明，利用双谱的非高斯强度特性的特征提取方法非常敏感，可以将正常运行条件与故障变化，非常清晰地识别风力涡轮机的轴承故障。

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