A key issue, which influences the applications of magnetic flux leakage testing, is defect quantification. There have been many research on the relationship between width, depth and magnetic flux leakage of slot defec...A key issue, which influences the applications of magnetic flux leakage testing, is defect quantification. There have been many research on the relationship between width, depth and magnetic flux leakage of slot defect. However, the length factor is often ignored. The relationship between characteristics of defect leakage field and defect length was investigated. The magnetic flux leakages of a series of plate specimens with the same width, same depth and different length slot defects were tested under the same magnetizing conditions. Testing results show that defect length is an important parameter needed to consider in quantifying defects.展开更多
Carbon-based solid lubricants are excellent options to reduce friction and wear,especially with the carbon capability to adopt different allotropes forms.On the macroscale,these materials are sheared on the contact al...Carbon-based solid lubricants are excellent options to reduce friction and wear,especially with the carbon capability to adopt different allotropes forms.On the macroscale,these materials are sheared on the contact along with debris and contaminants to form tribolayers that govern the tribosystem performance.Using a recently developed advanced Raman analysis on the tribolayers,it was possible to quantify the contactinduced defects in the crystalline structure of a wide range of allotropes of carbon-based solid lubricants,from graphite and carbide-derived carbon particles to multi-layer graphene and carbon nanotubes.In addition,these materials were tested under various dry sliding conditions,with different geometries,topographies,and solid-lubricant application strategies.Regardless of the initial tribosystem conditions and allotrope level of atomic ordering,there is a remarkable trend of increasing the point and line defects density until a specific saturation limit in the same order of magnitude for all the materials tested.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
基金This work was financially supported by the National Natural Science Foundation of China(Grant No.50001006,50305017)China Postdoctoral Science Foundation.
文摘A key issue, which influences the applications of magnetic flux leakage testing, is defect quantification. There have been many research on the relationship between width, depth and magnetic flux leakage of slot defect. However, the length factor is often ignored. The relationship between characteristics of defect leakage field and defect length was investigated. The magnetic flux leakages of a series of plate specimens with the same width, same depth and different length slot defects were tested under the same magnetizing conditions. Testing results show that defect length is an important parameter needed to consider in quantifying defects.
基金the following Brazilian agencies for funding this research:CNPq,CAPES,BNDES and the Chilean agency ANID Vinculación Internacional FOVI220096(No.72190023)as well as Nidec Global Appliance/Embraco.
文摘Carbon-based solid lubricants are excellent options to reduce friction and wear,especially with the carbon capability to adopt different allotropes forms.On the macroscale,these materials are sheared on the contact along with debris and contaminants to form tribolayers that govern the tribosystem performance.Using a recently developed advanced Raman analysis on the tribolayers,it was possible to quantify the contactinduced defects in the crystalline structure of a wide range of allotropes of carbon-based solid lubricants,from graphite and carbide-derived carbon particles to multi-layer graphene and carbon nanotubes.In addition,these materials were tested under various dry sliding conditions,with different geometries,topographies,and solid-lubricant application strategies.Regardless of the initial tribosystem conditions and allotrope level of atomic ordering,there is a remarkable trend of increasing the point and line defects density until a specific saturation limit in the same order of magnitude for all the materials tested.
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.
