A Novel Approach for Denoising Magnetic Flux Leakage Signals of Steel Wire Ropes via Synchrosqueezing Wavelet Transform and Dynamic Time–Frequency Masking

Magnetic flux leakage (MFL) signals in steel wire rope defect detection are often corrupted by structural noise and environmental interference, leading to reduced defect recognition accuracy. This study proposes a denoising approach combining synchrosqueezing wavelet transform (SST) with dynamic time–frequency masking to enhance signal quality. The method first employs SST to redistribute time–frequency coefficients, improving resolution and highlighting defect-related energy concentrations. A dynamic masking strategy is then introduced to adaptively suppress noise by leveraging local energy statistics. Experimental results on a constructed dataset show that the proposed method achieves a signal-to-noise ratio (SNR) improvement compared to traditional wavelet denoising. This approach provides an effective solution for real-time monitoring of wire rope defects in industrial applications.