A wire rope defects detection method based on permanent magnet excitation is proposed.A detection system,mainly composed of permanent magnet excitation,distance detection,multi-sensor magnetic flux leakage signal acqu...A wire rope defects detection method based on permanent magnet excitation is proposed.A detection system,mainly composed of permanent magnet excitation,distance detection,multi-sensor magnetic flux leakage signal acquisition and data analysis device,is set up.According to the different characteristics of the multi-sensor magnetic flux leakage signal,the localized fault(LF)and loss of metallic cross-sectional area(LMA)signal is separated,and then the two defects can be detected.The experiments show that the method can effectively detect the two defects when they appear simultaneously on the wire rope.展开更多
Various structural defects deteriorate tunnel operation status and threaten public safety.Current tunnel inspection methods face problems of low efficiency,high equipment expense,and difficult data management.Combinin...Various structural defects deteriorate tunnel operation status and threaten public safety.Current tunnel inspection methods face problems of low efficiency,high equipment expense,and difficult data management.Combining the deep learning model and the 3D reconstruction method based on structure from motion(SfM),this paper proposes a novel SfM-Deep learning method for tunnel inspection.The high-quality 3D tunnel model is constructed by using images taken every 1 m along the longitudinal direction.The instance segmentation of leakage in longitudinal images is realized using the mask region-based convolutional neural network deep learning model.The SfM-Deep learning method projects the texture of the images after defect recognition to the 3D model and realizes the visualization of leakage defects.By projecting the model to the design cylindrical surface and expanding it,the tunnel leakage area is quantified.Through its practical application in a Shanghai metro shield tunnel,the reliability of the proposed method was verified.The novel SfM-Deep learning method can help engineers efficiently carry out intelligent tunnel detection.展开更多
文摘A wire rope defects detection method based on permanent magnet excitation is proposed.A detection system,mainly composed of permanent magnet excitation,distance detection,multi-sensor magnetic flux leakage signal acquisition and data analysis device,is set up.According to the different characteristics of the multi-sensor magnetic flux leakage signal,the localized fault(LF)and loss of metallic cross-sectional area(LMA)signal is separated,and then the two defects can be detected.The experiments show that the method can effectively detect the two defects when they appear simultaneously on the wire rope.
基金supported by the Key Field Science and Technology Project of Yunnan Province(Grant No.202002AC080002)the National Natural-Science Foundation of China(Grant No.52078377).
文摘Various structural defects deteriorate tunnel operation status and threaten public safety.Current tunnel inspection methods face problems of low efficiency,high equipment expense,and difficult data management.Combining the deep learning model and the 3D reconstruction method based on structure from motion(SfM),this paper proposes a novel SfM-Deep learning method for tunnel inspection.The high-quality 3D tunnel model is constructed by using images taken every 1 m along the longitudinal direction.The instance segmentation of leakage in longitudinal images is realized using the mask region-based convolutional neural network deep learning model.The SfM-Deep learning method projects the texture of the images after defect recognition to the 3D model and realizes the visualization of leakage defects.By projecting the model to the design cylindrical surface and expanding it,the tunnel leakage area is quantified.Through its practical application in a Shanghai metro shield tunnel,the reliability of the proposed method was verified.The novel SfM-Deep learning method can help engineers efficiently carry out intelligent tunnel detection.
