基于局部均值分解与拉普拉斯特征映射的滚动轴承故障诊断方法

摘要

针对滚动轴承非平稳振动信号的特征提取及维数优化问题，提出了融合局部均值分解与拉普拉斯特征映射的轴承故障诊断方法.首先，通过局部均值分解对非平稳振动信号进行平稳化分解，提取乘积函数分量、瞬时频率及瞬时幅值的高维信号特征集；然后，将高维特征集作为拉普拉斯特征映射算法的学习对象，提取轴承高维故障特征集的内在流形分布，以获得敏感、稳定的轴承振动特征参数，实现基于非平稳振动信号分析的滚动轴承故障特征提取；最后，结合支持向量分类模型量化 LMD-LE 方法的特征提取效果，实现不同状况下的轴承故障分类.轴承故障样本分类识别平均正确率达到91．17％，表明LMD-LE方法有效实现了高维局部均值分解特征集合的降噪，所提取的特征矩阵对轴承故障特征描述准确.%A new diagnosis method for feature extraction of non-stationary vibration signals and fault classification of rolling bearings was proposed based on LMD and LE.Firstly,the non-stationary vibration signals of rolling bearings were decomposed into several product functions with LMD.Then, dimensional fault feature sets were established by the time-frequency domain features of product func-tion,instantaneous frequency and amplitude.Secondly,LE was introduced to extract the sensitive and stable characteristic parameters to describe the running states of rolling bearings effectively and accu-rately.Finally,support vector machine classification model was built to realize the classification of fault bearings.For test samples classification,the average prediction accuracy is as 9 1 .1 7%.It means that the fusion method of the LMD and LE is suitable and feasible for the bearing fault feature extrac-tion.