摘要VPMCD(Variable Predictive Model based Class Discriminate)是一种新的模式识别方法，它充分利用从原始数据中所提取的特征值之间的相互内在关系建立数学模型，从而进行模式识别。论文将VPMCD结合排列熵(Permutation Entropy，简称PE)方法应用于滚动轴承故障诊断。首先采用ITD(Intrinsic Time-scale Decomposition)对滚动轴承振动信号进行分解，得到若干个固有旋转(Proper Rotation，简称PR)分量，并对包含主要故障信息的PR分量提取排列熵作为故障特征值；然后，对VPMCD分类器进行训练；最后，采用VPMCD分类器进行故障识别和分类。实验数据的分析结果表明该方法能够有效地应用于滚动轴承故障诊断。
Abstract：Variable Predictive Model based Class Discriminate (VPMCD) is a pattern recognition method, which makes full use of the inner relations among characteristic values extracted from the original data to recognize models. In this paper VPMCD is combined with Permutation Entropy (PE) to diagnosis the rolling bear fault. Firstly, rolling bearing vibration signals are adaptively decomposed into a sum of Proper Rotation (PR) components by using ITD and the permutation entropy of PR components which contain the main fault information are extracted as characteristic values. Secondly, the characteristic values are used to train the parameters of VPMCD. Finally, the VPMCD classifier is used to recognize and classify the faults. The experimental results show that this method can be effectively applied to rolling bearing fault diagnosis.
2014, Vol. 33 
