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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.10952 |
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| _version_ | 1866913794991587328 |
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| author | You, Siyu Yang, Leilei Kuang, Zixu Gou, Huayi Zhang, Longlong Liu, Zhiliang |
| author_facet | You, Siyu Yang, Leilei Kuang, Zixu Gou, Huayi Zhang, Longlong Liu, Zhiliang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_10952 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Signal Matrix-Based Local Flaw Detection Framework for Steel Wire Ropes Using Convolutional Neural Networks You, Siyu Yang, Leilei Kuang, Zixu Gou, Huayi Zhang, Longlong Liu, Zhiliang Signal Processing 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. |
| title | A Signal Matrix-Based Local Flaw Detection Framework for Steel Wire Ropes Using Convolutional Neural Networks |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2504.10952 |