Adaptive Multi-Scale Bayesian Framework for MFL Inspection of Steel Wire Ropes
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| Опубликовано в:: | Machines vol. 12, no. 11 (2024), p. 801 |
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| Главный автор: | |
| Другие авторы: | , , |
| Опубликовано: |
MDPI AG
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| Предметы: | |
| Online-ссылка: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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MARC
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| 001 | 3133147127 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2075-1702 | ||
| 024 | 7 | |a 10.3390/machines12110801 |2 doi | |
| 035 | |a 3133147127 | ||
| 045 | 2 | |b d20240101 |b d20241231 | |
| 084 | |a 231531 |2 nlm | ||
| 100 | 1 | |a Li, Xiaoping | |
| 245 | 1 | |a Adaptive Multi-Scale Bayesian Framework for MFL Inspection of Steel Wire Ropes | |
| 260 | |b MDPI AG |c 2024 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Magnetic flux leakage (MFL) technology is widely used in steel wire rope (SWR) inspection for non-destructive testing. However, accurate defect characterization requires advanced signal processing techniques to handle complex noise conditions and varying defect types. This paper presents a novel adaptive multi-scale Bayesian framework for MFL signal analysis in SWR inspection. Our approach integrates discrete wavelet transform with adaptive thresholding and multi-scale feature fusion, enabling simultaneous detection of minute defects and large-area corrosion. To validate our method, we implemented a four-channel MFL detection system and conducted extensive experiments on both simulated and real-world datasets. Compared with state-of-the-art methods, including long short-term memory (LSTM), attention mechanisms, and isolation forests, our approach demonstrated significant improvements in precision, recall, and F1 score across various tolerance levels. The proposed method showed superior detection performance, with an average precision of 91%, recall of 89%, and an F1 score of 0.90 in high-noise conditions, surpassing existing techniques. Notably, our method showed superior performance in high-noise environments, reducing false positive rates while maintaining high detection sensitivity. While computational complexity in real-time processing remains a challenge, this study provides a robust solution for non-destructive testing of SWR, potentially improving inspection efficiency and defect localization accuracy. Future work will focus on optimizing algorithmic efficiency and exploring transfer learning techniques for enhanced adaptability across different non-destructive testing (NDT) domains. This research not only advances signal processing and anomaly detection technology but also contributes to enhancing safety and maintenance efficiency in critical infrastructure. | |
| 653 | |a Magnetic flux | ||
| 653 | |a Venus | ||
| 653 | |a Signal analysis | ||
| 653 | |a Wire rope | ||
| 653 | |a Nondestructive testing | ||
| 653 | |a Wavelet transforms | ||
| 653 | |a Defects | ||
| 653 | |a Adaptability | ||
| 653 | |a Art techniques | ||
| 653 | |a Magnetic fields | ||
| 653 | |a Signal processing | ||
| 653 | |a Discrete Wavelet Transform | ||
| 653 | |a Efficiency | ||
| 653 | |a Data analysis | ||
| 653 | |a Digital signal processors | ||
| 653 | |a Energy consumption | ||
| 653 | |a Wavelet analysis | ||
| 653 | |a Corrosion | ||
| 653 | |a Recall | ||
| 653 | |a Machine learning | ||
| 653 | |a Steel wire | ||
| 653 | |a Inspection | ||
| 653 | |a Bayesian analysis | ||
| 653 | |a Fourier transforms | ||
| 653 | |a Experiments | ||
| 653 | |a Noise sensitivity | ||
| 653 | |a Sensors | ||
| 653 | |a Working conditions | ||
| 653 | |a Magnetic flux leakage testing | ||
| 653 | |a Design | ||
| 653 | |a Methods | ||
| 653 | |a Complexity | ||
| 653 | |a Anomalies | ||
| 653 | |a Real time | ||
| 653 | |a Critical infrastructure | ||
| 700 | 1 | |a Sun, Yujie | |
| 700 | 1 | |a Liu, Xinyue | |
| 700 | 1 | |a Zhang, Shaoxuan | |
| 773 | 0 | |t Machines |g vol. 12, no. 11 (2024), p. 801 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3133147127/abstract/embedded/75I98GEZK8WCJMPQ?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3133147127/fulltextwithgraphics/embedded/75I98GEZK8WCJMPQ?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3133147127/fulltextPDF/embedded/75I98GEZK8WCJMPQ?source=fedsrch |