基于LMD-CM-PCA的滚动轴承故障诊断方法

Roller Bearing Fault Diagnosis Method Based on LMD-CM-PCA

为提高在非平稳工况下对滚动轴承故障的直观辨识能力,笔者提出基于LMD-CM-PCA的故障诊断方法。首先,对滚动轴承振动信号进行局部均值分解(local mean decomposition,简称LMD),提取乘积函数(product function,简称PF)矩阵;然后,计算PF矩阵与原振动信号的皮氏相关系数(pearson product-moment correlation coefficient,简称PPCC),将PFs对应的PPCC代入相关熵模型得到PF的相关熵矩阵(correntropy matrix,简称CM),CM经主成分分析(principal component analysis,简称PCA)进行特征变换得到融合相关熵矩阵(integrated correntropy matrix,简称ICM)。分别在轻微和严重故障时,对滚动轴承不同工况下的振动样本进行交叉混合,并计算其ICM。结果证明,ICM在可视维度比传统特征(如:能量矩和谱峭度)的融合特征更能隔离工况对故障可分性的干扰。LMD-CM-PCA方法为滚动轴承故障的直观辨识提供了技术支持,在故障诊断方面具有良好的应用前景。

It is necessary to improve the visual robust fault identification ability of roller bearing in non-stationary operating condition. To achieve it, LMD-CM-PCA approach was proposed. First, based on roller bearing vibration acceleration signals, local mean decomposition(LMD) was applied to extract product function (PF) sample matrix. Second, the discrete correntropy and Pearson product-moment correlation coefficient (PPCC) of PF and primary signal were calculated. Correntropy was modified by PPCC as the amplitude modulation (AM) of correntropy. Then, the correntropy matrix (CM) of the samples was constructed with AM-correntropy being itselements. Finally, principal component analysis (PCA) was employed to implement the integration of CM with the largest variance accumulated contribution rate as the evaluation index. Integrated CM (ICM) of vibration datum under mixed operating conditions was calculated in slight fault and serious fault situations both. The visual results indicated that ICM could isolate operating condition better and separate faults under different fault severity levels more robustly than traditional characteristics, such as energy moment and spectral kurtosis, do. Above all, application of ICM,like roller bearing fault features provides more effective technical support for roller bearing fault intuitively diagnosis so that it can support applications in fault diagnosis and safety early warning fields. © 2017, Editorial Department of JVMD. All right reserved.