Steel Wire Rope Damage Width Identification Method Based on Residual Networks and Multi-Channel Feature Fusion
In order to ensure the safety of steel wire rope in various application scenarios, it is particularly important to quantitatively detect the defects of wire rope. Complex detection conditions affect the detection efficiency of wire rope. Therefore, based on the magnetic flux leakage method, this stu...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-10-01
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| Series: | Machines |
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| Online Access: | https://www.mdpi.com/2075-1702/12/11/744 |
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| author | Yan Peng Junde Liu Junjie He Yongjun Qiu Xie Liu Le Chen Fengfeng Yang Bulong Chen Bin Tang Yuhan Wang |
| author_facet | Yan Peng Junde Liu Junjie He Yongjun Qiu Xie Liu Le Chen Fengfeng Yang Bulong Chen Bin Tang Yuhan Wang |
| author_sort | Yan Peng |
| collection | DOAJ |
| description | In order to ensure the safety of steel wire rope in various application scenarios, it is particularly important to quantitatively detect the defects of wire rope. Complex detection conditions affect the detection efficiency of wire rope. Therefore, based on the magnetic flux leakage method, this study proposes a method to identify the damage width of steel wire rope for multi-channel fusion of a Hall sensor array. Firstly, the Hall sensor array is used to capture the magnetic flux leakage data of steel wire rope; then, continuous wavelet transform is used to decompose the original data, and moving average filtering is used to denoise each component; the denoised components are merged and converted into a time spectrum, and the time spectrum is classified by ResNet50 image classification model to realize the detection of wire rope damage width. According to the dataset used in this study, the results show that the proposed method performs best in the mainstream noise reduction model; detection accuracy for the width of damage in steel wire ropes is 97%, which proves that the proposed method is effective and feasible. |
| format | Article |
| id | doaj-art-aede402fbeea4a6385c1a33ce6fb092d |
| institution | Kabale University |
| issn | 2075-1702 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj-art-aede402fbeea4a6385c1a33ce6fb092d2024-11-26T18:10:55ZengMDPI AGMachines2075-17022024-10-01121174410.3390/machines12110744Steel Wire Rope Damage Width Identification Method Based on Residual Networks and Multi-Channel Feature FusionYan Peng0Junde Liu1Junjie He2Yongjun Qiu3Xie Liu4Le Chen5Fengfeng Yang6Bulong Chen7Bin Tang8Yuhan Wang9Chongqing Special Equipment Inspection and Research Institute, Chongqing 400021, ChinaChongqing Special Equipment Inspection and Research Institute, Chongqing 400021, ChinaChongqing Key Laboratory of Optical Fiber Sensor and Photoelectric Detection, Chongqing University of Technology, Chongqing 400054, ChinaChongqing Special Equipment Inspection and Research Institute, Chongqing 400021, ChinaChongqing Key Laboratory of Optical Fiber Sensor and Photoelectric Detection, Chongqing University of Technology, Chongqing 400054, ChinaChongqing Key Laboratory of Optical Fiber Sensor and Photoelectric Detection, Chongqing University of Technology, Chongqing 400054, ChinaChongqing Key Laboratory of Optical Fiber Sensor and Photoelectric Detection, Chongqing University of Technology, Chongqing 400054, ChinaChongqing Key Laboratory of Optical Fiber Sensor and Photoelectric Detection, Chongqing University of Technology, Chongqing 400054, ChinaChongqing Key Laboratory of Optical Fiber Sensor and Photoelectric Detection, Chongqing University of Technology, Chongqing 400054, ChinaChongqing Key Laboratory of Optical Fiber Sensor and Photoelectric Detection, Chongqing University of Technology, Chongqing 400054, ChinaIn order to ensure the safety of steel wire rope in various application scenarios, it is particularly important to quantitatively detect the defects of wire rope. Complex detection conditions affect the detection efficiency of wire rope. Therefore, based on the magnetic flux leakage method, this study proposes a method to identify the damage width of steel wire rope for multi-channel fusion of a Hall sensor array. Firstly, the Hall sensor array is used to capture the magnetic flux leakage data of steel wire rope; then, continuous wavelet transform is used to decompose the original data, and moving average filtering is used to denoise each component; the denoised components are merged and converted into a time spectrum, and the time spectrum is classified by ResNet50 image classification model to realize the detection of wire rope damage width. According to the dataset used in this study, the results show that the proposed method performs best in the mainstream noise reduction model; detection accuracy for the width of damage in steel wire ropes is 97%, which proves that the proposed method is effective and feasible.https://www.mdpi.com/2075-1702/12/11/744multi-channel fusionResNet 50wire rope defect detectioncontinuous wavelet transform |
| spellingShingle | Yan Peng Junde Liu Junjie He Yongjun Qiu Xie Liu Le Chen Fengfeng Yang Bulong Chen Bin Tang Yuhan Wang Steel Wire Rope Damage Width Identification Method Based on Residual Networks and Multi-Channel Feature Fusion Machines multi-channel fusion ResNet 50 wire rope defect detection continuous wavelet transform |
| title | Steel Wire Rope Damage Width Identification Method Based on Residual Networks and Multi-Channel Feature Fusion |
| title_full | Steel Wire Rope Damage Width Identification Method Based on Residual Networks and Multi-Channel Feature Fusion |
| title_fullStr | Steel Wire Rope Damage Width Identification Method Based on Residual Networks and Multi-Channel Feature Fusion |
| title_full_unstemmed | Steel Wire Rope Damage Width Identification Method Based on Residual Networks and Multi-Channel Feature Fusion |
| title_short | Steel Wire Rope Damage Width Identification Method Based on Residual Networks and Multi-Channel Feature Fusion |
| title_sort | steel wire rope damage width identification method based on residual networks and multi channel feature fusion |
| topic | multi-channel fusion ResNet 50 wire rope defect detection continuous wavelet transform |
| url | https://www.mdpi.com/2075-1702/12/11/744 |
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