From Time to Channels: Robust and Efficient Local Flaw Detection in Steel Wire Ropes Using Tri-Axis MFL Signals

Steel wire ropes (SWRs) are critical load-bearing components whose local flaws (LFs) pose serious safety risks. Magnetic flux leakage (MFL) inspection commonly detects LFs from temporal or axial morphology, which can change with the sensing axis and operating condition. We show instead that LF responses remain localized over neighboring channels of a circular array. This observation motivates a channel-feature-oriented (CFO) detector that removes smooth channel backgrounds, applies circular matched filtering, and fuses spatially co-located tri-axis responses. Experiments performed on real-world equipment show that CFO attains the highest localization performance among three representative baselines, reaching F1@0.5/F1@0.7 scores of 73.9%/59.5%. It attains the best temporal localization performance under all four conditions and achieves at least 19.4 times the throughput of the evaluated signal-processing baselines. These results demonstrate that circular channel locality provides a robust LF representation across the evaluated operating conditions.