基于改进HHT能量熵和SVM的滚动轴承故障诊断  

Fault Diagnosis of Rolling Bearing based on Improved HHT Energy Entropy and SVM

作　　者：周小龙[1] 姜振海[2] 马风雷[2] 

机构地区：[1]东北电力大学工程训练教学中心,吉林吉林132012 [2]长春工业大学机电工程学院,吉林长春130012

出　　处：《机械传动》2016年第12期164-168,共5页Journal of Mechanical Transmission

基　　金：吉林省教育厅科技发展项目(2014124)

摘　　要：针对滚动轴承振动信号非平稳性以及故障诊断过程中试验样本总量少的特点,提出一种基于改进希尔伯特-黄变换能量熵和支持向量机相结合的滚动轴承故障诊断方法。首先,利用改进经验模态将不同状态下滚动轴承的振动信号分解成一系列固有模态函数,并采用敏感模态函数判别算法找出对故障信息敏感的固有模态函数;其次,将敏感模态函数分量的能量熵作为输入建立支持向量机;最后,利用支持向量机对滚动轴承的工作状态和故障类型进行识别。试验结果表明,该方法可有效应用于滚动轴承的故障诊断,为该类信号的故障诊断提供了一种切实可行的方法。Aiming at the non -stationary ples are always in a small number in its fault feature of the rolling beating vibration signal and the fault samdiagnosis, a rolling beating fault diagnosis method based on improved Hilbert -Huang transform energy entropy and support vector machine is proposed. Firstly, the vibration signal in different condition is decomposed by improved empirical mode decomposition, and the intrinsic mode functions are obtained and sensitive mode functions are selected by the sensitivity evaluation method. Then, the energy entropy of sensitive mode functions serve as input vectors of support vector machine. Finally, by using support vector machine to identify the rolling bearing fault pattern and condition. The experiment results show that this method can identify rolling bearing fault patterns effectively and offer a practical method for its fauh diagnosis.

关 键 词：希尔伯特-黄变换 能量熵 支持向量机 滚动轴承 故障诊断 

分 类 号：TH133.33[机械工程—机械制造及自动化]