Multi-channel fusion scale transformed signals with magnetic leakage for damage detection in steel wire ropes

The timely identification and diagnosis of defects in Steel Wire Rope (SWR) is increasingly critical for ensuring structural integrity, operational safety, and maintenance efficiency across diverse industrial applications, including elevators, mining equipment, bridges, and cable-supported structures where unexpected failures could lead to catastrophic consequences. Current methodologies face significant challenges in detecting low-amplitude faults due to lift-off effects and complex noise environments, while existing localization frameworks struggle to balance detection accuracy with false omission rates. This research proposes a novel Multi-channel Fusion Scale Transformation (MCFST) approach utilizing magnetic flux leakage signals to address the challenges in detecting and localizing Local Faults (LFs), particularly those with diminished amplitude due to inherent weakness or lift-off effects amid comparable ambient noise. The proposed MFST develops a three-fold framework: a morphological-based signal enhancement technique that amplifies LF signals while preserving their essential characteristics, a Channel Shuffle and Fusion paradigm for ambient noise mitigation, and a computationally efficient localization framework combining template matching algorithms with LF-specific luminance characteristics. Experimental results demonstrate superior performance in detecting and precisely localizing LFs under challenging conditions, offering substantial improvements over existing approaches in both detection accuracy and computational efficiency.