基于EEMD-TEO熵的高速列车轴承故障诊断方法

Diagnostic Method for High-Speed Train Bearing Fault Based on EEMD-TEO Entropy

为了解决高速列车轴承早期故障中低频信号的类间分离性较弱、保持架故障难以识别等的问题,提出了基于Teager能量算子(Teager energy operator,TEO)聚合经验模态分解(ensemble empirical mode decomposition,EEMD)熵的自适应诊断方法.该方法将EEMD、样本熵、TEO相结合,利用EEMD的自适应性得到固有模态(intrinic mode function,IMF)信号,用改进的TEO从IMF中提取得到样本熵,使用支持向量机(support vector machine,SVM)判断轴承工作状态与故障类型;讨论了EEMD能量熵、EEMD奇异值熵、EEMD-TEO时频熵生成的故障特征向量以及该向量在SVM中识别结果;对正常轴承、保持架故障、滚动体故障3种状态的轴承样本数据进行了故障诊断.研究结果表明:对3种轴承的故障识别率可以达到98%,较传统的经验模态熵识别率提高了2.6%,该方法可用作高速列车轴承状态诊断.

To overcome the limitations of the between-class separateness of low-frequency signals of high-speed train bearing faults as well as the problem of fault identification of the cage,an adaptive diagnostic method based on the Teager energy operator（TEO） and ensemble empirical mode decomposition（EEMD） entropy is proposed. This method combines the EEMD,sample entropy,and the TEO,and the intrinsic mode function（IMF） signal is obtained by the self-adaptation of EEMD,and then the sample entropy is obtained from the IMF using the improved TEO. Finally,the support vector machine（SVM） is used to determine the working state and fault type of the bearing. The fault eigenvectors of the EEMD energy entropy,EEMD singular value entropy,EEMD-TEO time-frequency entropy generation,and the identification results of this vector in the SVM are discussed. The method was used to diagnosethefault ofbearingsviathedata in three states： normal bearing,retainer bearing,and fault of the rolling body. The results show that the fault recognition rate for the three bearing states can reach 98%,increased by 2. 6% compared to the traditional empirical mode entropy,this method canbe used for the diagnosis of high-speed train bearings.