基于乘积函数相关熵的滚动轴承故障辨识方法

Roller Element Bearing Fault Identification Method Based on Product Function Correntropy

为了提高多工况下对滚动轴承的故障辨识能力,本文提出以乘积函数相关熵为故障特征的滚动轴承故障辨识方法,并利用最小二乘支持向量机(Least Square Support Vector Machine,LSSVM)实现自动辨识.首先对预处理的轴承振动信号进行局部均值分解,提取乘积函数(Product Function,PF),然后计算PF与原始信号的皮尔逊积矩相关系数熵,进而根据离散变量相关熵的估计模型得到乘积函数相关熵(Product Function Correntropy,PFC).以PFC为故障特征,结合LSSVM实现滚动轴承的故障识别.多组工况下的滚动轴承状态辨识实验证实了PFC比经典故障特征具有更高的故障辨识效率;另外改变工况参数提取轴承振动数据,验证了PFC-LSSVM方法具有更好的鲁棒辨识能力.综上所述,本文验证了LMD-PFC-LSSVM方法的高效性和实用性,为提高复杂工况下在线故障诊断能力提供了可靠的技术支持,具有广阔的应用前景.

To improve the ability of roller bearing fault diagnosis under multi rotating condition,this paper proposes a roller bearing fault identification approach that Product Function Correntropy( PFC) is as fault feature and Least Square Support Vector Machine( LSSVM) is applied to implement fault identification of roller bearings under multi-stationary working conditions.Firstly,roller bearing vibration acceleration signal is extracted from motor bearing test bench and the vibration signal is dealt with LMD to extract Product Functions( PF). Then the mathematical model of correntropy of PF and primary signal modified by Pearson Correlation Coefficient Entropy( PCCE) will be calculated,which is named Product Function Correntropy( PFC). Drawing support from LSSVM the safety and failure types identification is achieved.Through the bearing identification experiments in different operating conditions,it is verified that PFC-SVM generates higher diagnosis accuracy than typical fault features. Meanwhile,it is proved that PFC has better robustness than typical fault features when roller bearing operates under mixed condition. Above all,the higher efficiency and availability of LMD-PFC-SVM is confirmed from the experiment consequence. It can be concluded that LMD-PFC-SVM is a reliable technology for roller bearing fault diagnosis online under complicated operating conditions and possesses the broad application prospect.