A Frequency-Domain Blind Deconvolution Method Based on Weight Feature Extraction and Its Application in Rolling Bearing Fault Diagnosis

Rolling bearing faults typically exhibit sideband structures in the frequency domain, yet conventional blind deconvolution methods operate in the time domain and rely on prior fault periods. To reduce this dependence and broaden applicability, this paper presents a frequency-domain blind deconvolution method based on weight feature extraction (WFE-FDBD). The method constructs rectangular pulse weights with a finite bandwidth to mitigate the effects of limited frequency resolution and minor sideband fluctuations. By incorporating the correlated kurtosis index, an inverse filter with adaptive period adjustment is built, enabling automatic selection of the fault period. Operating directly in the frequency domain, the proposed approach effectively enhances fault-related harmonics and suppresses noise interference. Both simulated and experimental signals validate the WFE-FDBD method, demonstrating its capability to diagnose localized defects on inner and outer raceways of rolling bearings.