Novel Fault Detection Method for Rolling Bearings Based on Improved Variational Modal Decomposition Method

To enhance the precision of rolling bearing fault detection and lessen the likelihood of safety mishaps, this paper proposes a fault detection method grounded in improved variational mode decomposition. This technique initially employs the Northern Eagle Algorithm to determine the optimal parameter value for variational mode decomposition, subsequently decomposing the signal. This is followed by the utilization of the Spearman correlation coefficient to differentiate between the effective component and the noise-dominant component. Finally, the wavelet packet decomposition is adopted to filter noise and yield the Hilbert envelope spectrum of the de-noised signal to ascertain the bearing’s health status based on the extraction of characteristic fault frequencies and harmonics. The experimental findings illustrate that the enhanced variational mode decomposition technique not only escalates the inner ring signal’s signal-to-noise ratio from −10.844 dB to 8.4471 dB and the outer ring signal’s ratio from −4.5852 dB to 3.0997 dB but also reduces the error of outer ring fault detection from 3.14% to 0.37%, and improves the frequency of inner ring fault detection from a feature extraction inability to an error frequency of 0.45%.