Enhanced Frequency-Domain Sparse Constraint Embedding Variational Mode Extraction for Rolling Bearing Fault Detection

Rolling bearings are critical components in rotating machinery and are widely used in modern industrial applications. Timely and accurate fault detection of these bearings is essential for ensuring operational reliability. However, fault-induced periodic transients in bearings are often overshadowed by various interfering components in the original vibration signal, complicating the task of fault feature extraction. Variational mode extraction (VME) has shown considerable capability and flexibility in extracting bearing fault features. Nevertheless, VME primarily focuses on mode smoothness and spectral aliasing reduction of the residual signal, without accounting for the frequency-domain sparsity of fault features. As a result, VME fails to effectively enhance and isolate sparse fault features, especially in the presence of strong interference. To overcome this limitation, this study introduces an Enhanced Frequency-Domain Sparse Constraint Embedding Variational Mode Extraction (ECVME) method. ECVME improves the sparsity of fault features in the frequency domain by incorporating an enhanced frequency-domain sparse constraint. Specifically, this constraint utilizes a binary vector to promote the periodic group sparsity of fault transients, and a weighting scheme is applied to impose hierarchical penalties on the sparse coefficients corresponding to fault transients and harmonic components. Additionally, a non-convex penalty function is incorporated to further highlight the sparsity of fault transients. By integrating this enhanced frequency-domain sparse constraint, ECVME effectively extracts fault features while enhancing robustness against strong harmonic components and noise interference. Both simulation analysis and experimental validation demonstrate that ECVME outperforms several advanced comparison methods in fault feature extraction, successfully facilitating rolling bearing fault detection.