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

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 can be used for the diagnosis of high-speed train bearings.