AI 中文总结
本文提出基于三轴MFL信号的通道特征检测器，利用圆形阵列通道局部性，通过背景去除、匹配滤波和三轴融合，实现钢丝绳局部缺陷的鲁棒高效检测，性能优于基线。
AI 中文摘要
钢丝绳（SWRs）是关键的承重部件，其局部缺陷（LFs）构成严重的安全风险。磁通泄漏（MFL）检测通常根据时间或轴向形态来检测局部缺陷，而这些形态会随传感轴和工作条件而变化。我们反而表明，局部缺陷响应在圆形阵列的相邻通道上保持局部化。这一观察促使我们提出一种面向通道特征（CFO）的检测器，它去除平滑的通道背景，应用圆形匹配滤波，并融合空间共置的三轴响应。在真实设备上进行的实验表明，CFO在三个代表性基线中取得了最高的定位性能，F1@0.5/F1@0.7分数达到73.9%/59.5%。它在所有四种条件下均取得最佳的时间定位性能，并且吞吐量至少是所评估的信号处理基线的19.4倍。这些结果表明，圆形通道局部性在所评估的工作条件下提供了一种鲁棒的局部缺陷表示。
英文摘要
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.
