A Signal Matrix-Based Local Flaw Detection Framework for Steel Wire Ropes Using Convolutional Neural Networks

Steel wire ropes (SWRs) are critical load-bearing components in industrial applications, yet their structural integrity is often compromised by local flaws (LFs). Magnetic Flux Leakage (MFL) is a widely used non-destructive testing method that detects defects by measuring perturbations in magnetic fields. Traditional MFL detection methods suffer from critical limitations: one-dimensional approaches fail to capture spatial relationships across sensor channels, while multi-dimensional image-based techniques introduce interpolation artifacts and computational inefficiencies. This paper proposes a novel detection framework based on signal matrices, directly processing raw multi-channel MFL signals using a specialized Convolutional Neural Network for signal matrix as input (SM-CNN). The architecture incorporates stripe pooling to preserve channel-wise features and symmetric padding to improve boundary defect detection. Our model achieves state-of-the-art performance with 98.74% accuracy and 97.85% recall. Additionally, it demonstrates exceptional computational efficiency, processing at 87.72 frames per second (FPS) with a low inference latency of 2.6ms and preprocessing time of 8.8ms. With only 1.48 million parameters, this lightweight design supports real-time processing, establishing a new benchmark for SWR inspection in industrial settings.

Local Flaw Detection with Adaptive Pyramid Image Fusion Across Spatial Sampling Resolution for SWRs

The inspection of local flaws (LFs) in Steel Wire Ropes (SWRs) is crucial for ensuring safety and reliability in various industries. Magnetic Flux Leakage (MFL) imaging is commonly used for non-destructive testing, but its effectiveness is often hindered by the combined effects of inspection speed and sampling rate. To address this issue, the impacts of inspection speed and sampling rate on image quality are studied, as variations in these factors can cause stripe noise, axial compression of defect features, and increased interference, complicating accurate detection. We define the relationship between inspection speed and sampling rate as spatial sampling resolution (SSR) and propose an adaptive SSR target-feature-oriented (AS-TFO) method. This method incorporates adaptive adjustment and pyramid image fusion techniques to enhance defect detection under different SSR scenarios. Experimental results show that under high SSR scenarios, the method achieves a precision of 92.54% and recall of 98.41%. It remains robust under low SSR scenarios with a precision of 94.87% and recall of 97.37%. The overall results show that the proposed method outperforms conventional approaches, achieving state-of-the-art performance. This improvement in detection accuracy and robustness is particularly valuable for handling complex inspection conditions, where inspection speed and sampling rate can vary significantly, making detection more robust and reliable in industrial settings.