Incipient fault feature extraction of rolling bearings based on the MVMD and Teager energy operator

Aiming at the problems that the incipient fault of rolling bearings is difficult to recognize and the number of intrinsic mode functions (IMFs) decomposed by variational mode decomposition (VMD) must be set in advance and can not be adaptively selected, taking full advantages of the adaptive segmentation of scale spectrum and Teager energy operator (TEO) demodulation, a new method for early fault feature extraction of rolling bearings based on the modified VMD and Teager energy operator (MVMD-TEO) is proposed. Firstly, the vibration signal of rolling bearings is analyzed by adaptive scale space spectrum segmentation to obtain the spectrum segmentation support boundary, and then the number K of IMFs decomposed by VMD is adaptively determined. Secondly, the original vibration signal is adaptively decomposed into K IMFs, and the effective IMF components are extracted based on the correlation coefficient criterion. Finally, the Teager energy spectrum of the reconstructed signal of the effective IMF components is calculated by the TEO, and then the early fault features of rolling bearings are extracted to realize the fault identification and location. Comparative experiments of the proposed method and the existing fault feature extraction method based on Local Mean Decomposition and Teager energy operator (LMD-TEO) have been implemented using experimental data-sets and a measured data-set. The results of comparative experiments in three application cases show that the presented method can achieve a fairly or slightly better performance than LMD-TEO method, and the validity and feasibility of the proposed method are proved.