中文摘要：

针对滚动轴承的故障诊断问题，提出了一种基于集总经验模态分解（EEMD）、形态谱特征提取和核模糊 C 均值聚类（KFCMC）集成的故障诊断新方法。首先，对实测的滚动轴承振动信号进行 EEMD 分解，得到若干个代表不同振动模态的内禀模态函数（IMF）；其次，基于峭度、能量和均方差三个评价指标，从分解得到的若干个 IMF 分量中选出含有故障特征信息最丰富的3个 IMF 分量作为诊断用的数据源；然后在选定尺度范围内提取每个 IMF分量的形态谱平均值，将三个形态谱平均值构成一个三维特征向量，作为一个样本，形成样本集；最后，利用 KFC-MC 完成对滚动轴承不同故障的分类识别。此外，为了对比说明该方法的识别效果，还将振动信号用经验模态分解（EMD）方法进行分解，用模糊 C 均值聚类（FCMC）进行分类识别，结果表明所提方法的识别效果要优于 EMD 形态谱和 FCMC 相结合的方法。通过对实测的滚动轴承振动信号的实验验证，表明该方法可以实现对滚动轴承故障的有效诊断。

英文摘要：

Aiming at the fault diagnosis of rolling bearings,a fusion method based on ensemble empirical mode decomposition （EEMD）,morphological spectrum and kernel fuzzy C-means clustering （KFCMC）clustering is proposed.Firstly,a vibration signal is decomposed by EEMD to get several intrinsic mode functions （IMFs）which have physical meanings.Secondly,with a fusion evaluation method based on kurtosis,power and standard deviation,the three IMFs which are rich in fault features are selected as data source,the mean values of morphological spectrums in some scales of the three IMFs are extracted,and then the three values constitute a sample,thus sample set can be got.Lastly,all the samples of different working conditions are clustered by the KFCMC to diagnose the rolling bearing faults.In addition,the signals are also decomposed by empirical mode decomposition （EMD）,and the samples are also clustered by fuzzy C-means clustering （FCMC）,and the results show that the proposed method performs better than EMD and FCMC.The signals of the rolling bearings are tested and verified,and the conclusion is that the fusion method of EEMD and KFCMC is superior to that of EMD and FCMC.The proposed method can diagnosis the faults of rolling bearings efficiently.