中文摘要：

滚动轴承退化状态识别的关键在于特征提取和模式识别,局部特征尺度分解（local characteristic-scale decomposition,LCD）方法是一种新的时频分析方法,非常适用于滚动轴承振动信号的特征提取。基于变量预测模型的模式识别（Variable predictive model based class discriminate,VPMCD）方法是一种利用特征值之间的相互关系进行分类的模式识别方法,可以用于滚动轴承的退化状态识别。将LCD、VPMCD和高斯混合模型（Gaussain mixture model,GMM）相结合,提出了基于LCD和GMM-VPMCD混合模型的滚动轴承退化状态识别方法,首先对滚动轴承全寿命数据进行LCD分解并提取分量的特征值,然后利用GMM对全寿命数据的特征值进行聚类,将全寿命数据在时域上分成若干个退化状态,最后建立VPMCD模型并对测试数据进行分类,从而实现滚动轴承的退化状态识别。实验数据的分析结果表明,基于LCD的GMM-VPMCD混合模型可以有效实现滚动轴承的退化状态识别。

英文摘要：

The key of the degenerate state recognition of roller bearing is feature extraction and pattern recognition. Local characteristic-scale decomposition （LCD） is a new time-frequency analysis method, which is very suitably applied to the feature extraction of roller bearing vibration signal. Since variable predictive model based class discriminate （VPMCD） is a pattern recognition method in which the relationship between the feature values is adopted, it can be applied to the he degenerate state recognition of roller bearing. In this paper, LCD, VPMCD and Gaussain mixture model （GMM） are combined. Furthermore, the degenerate state recognition method of rolling beatings based on LCD and GMM-VPMCD hybrid model is proposed. Firstly, the whole life data of rolling beating is decomposed by LCD method and the feature values of the components are extracted ; then the feature values are clustered in time domain by using GMM and the whole life data is divided into some degenerate states in time domain; finally, the VPMCD model is constructed and applied to the degenerate state recognition of roller beating. The experiment results show that the GMM-VPMCD hybrid model based on LCD can be effectively applied to the degenerate state recognition of rolling bearing.