Enhanced rolling bearing fault diagnosis using DBO-VMD-KELM

Abstract To enhance the extraction of fault signal characteristics from rolling bearings and enhance fault classification precision, this paper presents an approach for diagnosing rolling bearing faults, integrating Variational Mode Decomposition (VMD) and Kernel Extreme Learning Machine (KELM). Furthermore, the Dung Beetle Optimizer (DBO) algorithm is employed to optimize VMD and KELM, addressing the challenge of determining critical parameters effectively. Initially, VMD is employed to break down the fault data from rolling bearings, and the pivotal parameters [k, α] of VMD are globally fine-tuned via the DBO algorithm, with the minimum envelope entropy serving as the optimization criterion. The ideal components of the decomposed intrinsic mode function (IMF) are then determined by comparing their minimal arranged entropy. Nine time-domain indicators are then computed to create the feature vector. Finally, in the KELM diagnostic model, the constructed feature vector is input, and DBO is combined with KELM for optimization to achieve the final fault diagnosis. Simulation results show that the fault diagnosis rate of KELM is 92%, and the fault diagnosis rate of KELM optimized by DBO is 99%, effectively identifying the fault categories of rolling bearings.