Early-Fault Feature Extraction for Rolling Bearings Based on Parameter-Optimized Variation Mode Decomposition

Bearing-vibration signals, characterized by strong non-stationarity, typically consist of multiple components. The periodic pulses related to bearing faults are frequently obscured by surrounding noise, and early bearing-fault vibrations are feeble, which complicates the extraction of inherent fault characteristics. The aim of this research is to develop an effective method for extracting early-fault characteristic frequencies in rolling bearings. VMD, short for variational mode decomposition, is an innovative technique rooted in the classical Wiener filter for analyzing signals that include multiple components. However, applying VMD to process real non-stationary signals still poses several challenges. A key challenge is that the internal parameters of VMD require manual setting prior to use. Aiming to mitigate this limitation, this paper introduces an enhanced variational mode decomposition approach utilizing the Chaotic Harris Hawk Optimization (CHHO) method. Average energy entropy is used as the optimization criterion in the CHHO–VMD algorithm to ascertain both the ideal mode count and its corresponding penalty factor. The original signal is further broken down into intrinsic mode functions (IMFs), with each IMF corresponding to a different frequency interval. In addition, IMF components are selected based on kurtosis and cross-correlation criteria to reconstruct fault signals. Finally, envelope demodulation is performed to reveal the fault characteristic frequencies. Experimental findings demonstrate that, as opposed to alternative techniques, this approach achieves superior performance in extracting early-fault frequencies in rolling bearings, offering a novel solution for early-fault feature extraction.