Coordinated approach fusing time-shift multiscale dispersion entropy and vibrational Harris hawks optimization-based SVM for fault diagnosis of rolling bearing

To fully mine the effective fault information and improve the fault diagnosis accuracy, a novel fault diagnosis approach for rolling bearings is proposed by integrating variational mode decomposition (VMD), time-shift multiscale dispersion entropy (TSMDE) and support vector machine (SVM) optimized by vibrational Harris hawks optimization algorithm (VHHO). Firstly, vibration signals with different fault types are decomposed into several intrinsic mode functions (IMFs) by VMD. Subsequently, the proposed TSMDE aggregating time-shift procedure and dispersion entropy is employed to extract multiscale fault features from IMFs. Afterwards, the proposed VHHO that adopts a periodic mutation mechanism to enhance the original Harris hawks optimization (HHO) is devoted to search the optimal parameters of SVM, with which different faults are recognized. Finally, simulations and applications are conducted to evaluate the proposed coordinated VMD-TSMDE-VHHO-SVM approach, and the results reveal that the proposed approach can achieve better diagnosis performance than other comparative ones.

Automated summary

A novel fault diagnosis approach for rolling bearings is proposed by integrating VMD, TSMDE, and SVM optimized by VHHO. Vibration signals are decomposed into IMFs by VMD, TSMDE is employed to extract multiscale fault features, and VHHO is used to optimize SVM parameters for fault recognition, achieving better diagnosis performance.