Adaptive Multi-Scale Bayesian Framework for MFL Inspection of Steel Wire Ropes
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 ada...
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MDPI AG
2024-11-01
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| Series: | Machines |
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| Online Access: | https://www.mdpi.com/2075-1702/12/11/801 |
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| author | Xiaoping Li Yujie Sun Xinyue Liu Shaoxuan Zhang |
| author_facet | Xiaoping Li Yujie Sun Xinyue Liu Shaoxuan Zhang |
| author_sort | Xiaoping Li |
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| description | 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. |
| format | Article |
| id | doaj-art-e97a41ed36ac4aa2a6ba8db6421aa10f |
| institution | Kabale University |
| issn | 2075-1702 |
| language | English |
| publishDate | 2024-11-01 |
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| series | Machines |
| spelling | doaj-art-e97a41ed36ac4aa2a6ba8db6421aa10f2024-11-26T18:11:07ZengMDPI AGMachines2075-17022024-11-01121180110.3390/machines12110801Adaptive Multi-Scale Bayesian Framework for MFL Inspection of Steel Wire RopesXiaoping Li0Yujie Sun1Xinyue Liu2Shaoxuan Zhang3School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, ChinaSchool of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, ChinaSchool of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, ChinaSchool of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, ChinaMagnetic 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.https://www.mdpi.com/2075-1702/12/11/801magnetic flux leakage (MFL)steel wire rope (SWR) inspectionmulti-scale analysisBayesian adaptive detectionwavelet transform |
| spellingShingle | Xiaoping Li Yujie Sun Xinyue Liu Shaoxuan Zhang Adaptive Multi-Scale Bayesian Framework for MFL Inspection of Steel Wire Ropes Machines magnetic flux leakage (MFL) steel wire rope (SWR) inspection multi-scale analysis Bayesian adaptive detection wavelet transform |
| title | Adaptive Multi-Scale Bayesian Framework for MFL Inspection of Steel Wire Ropes |
| title_full | Adaptive Multi-Scale Bayesian Framework for MFL Inspection of Steel Wire Ropes |
| title_fullStr | Adaptive Multi-Scale Bayesian Framework for MFL Inspection of Steel Wire Ropes |
| title_full_unstemmed | Adaptive Multi-Scale Bayesian Framework for MFL Inspection of Steel Wire Ropes |
| title_short | Adaptive Multi-Scale Bayesian Framework for MFL Inspection of Steel Wire Ropes |
| title_sort | adaptive multi scale bayesian framework for mfl inspection of steel wire ropes |
| topic | magnetic flux leakage (MFL) steel wire rope (SWR) inspection multi-scale analysis Bayesian adaptive detection wavelet transform |
| url | https://www.mdpi.com/2075-1702/12/11/801 |
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