Pore formation is a significant challenges in the advancement of laser additive manufacturing(LAM)technologies.To address this issue,image data-driven pore detection techniques have become a research focus.However,exi...Pore formation is a significant challenges in the advancement of laser additive manufacturing(LAM)technologies.To address this issue,image data-driven pore detection techniques have become a research focus.However,existing methods are constrained by reliance on a single detection environment(e.g.,consistent brightness)and fixed input image sizes,limiting their predictive accuracy and application scope.This paper introduces an in-novative a pore detection method based on a deep learning model for laser-directed energy deposition(L-DED).The proposed method leverages the deep learning model’s ability to extract feature information from melt pool images captured by a high-speed camera,enabling efficient pore detection under varying brightness conditions and diverse image sizes.The detection results demonstrate that,under varying brightness levels,the proposed model achieves a pore detection accuracy of approximately 93.5% and a root mean square error(RMSE)of 0.42 for local porosity prediction.Additionally,even with changes in input image size,the model maintains robust performance,achieving a detection accuracy of 96% for pore status detection and an RMSE value of 0.09 for local porosity prediction.This study not only addresses the limitations of traditional detection techniques but also broadens the scope of online detection technologies.It highlights the potential of deep learning in complex industrial settings and provides valuable insights for advancing defect detection research in related fields.展开更多
As the unique power entrance,the pantograph-catenary electrical contact system maintains the efficiency and reliability of power transmission for the high-speed train.Along with the fast development of high-speed rail...As the unique power entrance,the pantograph-catenary electrical contact system maintains the efficiency and reliability of power transmission for the high-speed train.Along with the fast development of high-speed railways all over the world,some commercialized lines are built for covering the remote places under harsh environment,especially in China;these environmental elements including wind,sand,rain,thunder,ice and snow need to be considered during the design of the pantograph-catenary system.The pantograph-catenary system includes the pantograph,the contact wire and the interface—pantograph slide.As the key component,this pantograph slide plays a critical role in reliable power transmission under dynamic condition.The fundamental material characteristics of the pantograph slide and contact wire such as electrical conductivity,impact resistance,wear resistance,etc.,directly determine the sliding electrical contact performance of the pantograph-catenary system;meanwhile,different detection methods of the pantograph-catenary system are crucial for the reliability of service and maintenance.In addition,the challenges brought from extreme operational conditions are discussed,taking the Sichuan-Tibet Railway currently under construction as a special example with the high-altitude climate.The outlook for developing the ultra-high-speed train equipped with the novel pantograph-catenary system which can address the harsher operational environment is also involved.This paper has provided a comprehensive review of the high-speed railway pantograph-catenary systems,including its progress,challenges,outlooks in the history and future.展开更多
In this study,newly harvested and aged rice seeds were analyzed to determine their aging process,identify the difference between artificially and naturally aged seeds,and develop a rapid,accurate,and non-destructive d...In this study,newly harvested and aged rice seeds were analyzed to determine their aging process,identify the difference between artificially and naturally aged seeds,and develop a rapid,accurate,and non-destructive detection method for water status and water distribution of rice seed with different vigor.To this end,an artificially accelerated aging test was conducted on the newly harvested rice seeds.Then,low-field nuclear magnetic resonance(LF-NMR)technology was applied to test the new(Shennong No.9816,2018),old(Shennong No.9816,2017),and artificially aged seeds(Shennong No.9816,2018).A standard germination test was conducted for three types of seeds.Finally,the differences of water status and distribution between rice seeds of different vigor were analyzed based on the standard germination test results and wave spectrometry information collected using LF-NMR.The results indicated that new seeds,old seeds,and the artificially accelerated aging rice seeds all exhibited two water phases,and the vigor of rice seeds after the artificial accelerated aging test was lower than that of new seeds.There were significant differences between the frequencies of bound water at the time of the peak and the time at the end of the peak for the three types of seeds.The two times showed an increasing trend for rice seeds with poor vigor,indicating that the ability of the water in the rice seeds having poor vigor to combine with other substances was weakened.There were significant differences between the distributions of free water peak end time for the three types of seeds.All the rice seeds with poor vigor exhibited a decreasing trend at this time,indicating that the freedom of free water inside the rice seed samples with poor vigor was weakened.The total water content of the artificially aged seeds and the aged seeds was higher than that of the new seeds,but the free water content increased from artificially aged seeds to new seeds to aged seeds.This indicates that LF-NMR technology is an effective detection method that can simply compare the differences in seed vitality with respect to water distribution as well as differentiate the seed internal water content of artificially aged and naturally aged seeds.展开更多
Diagnosing the operational status of High-voltage circuit breakers(HVCBs)is crucial for ensuring the safe and stable operation of the grid.Mechanical characteristic parameters are effective indicators for evaluating t...Diagnosing the operational status of High-voltage circuit breakers(HVCBs)is crucial for ensuring the safe and stable operation of the grid.Mechanical characteristic parameters are effective indicators for evaluating the performance of HVCBs.Recent studies have shown that the actions of the springs and cams in HVCBs can be used to detect the operational status of the mechanical mechanisms,which occur extremely quickly,usually in the speed of m/ms.In this paper,dynamic vision sensing technology was employed to rapidly and dynamically capture the movements of the springs and cam of the HPL245B1 HVCB.The data volume of a single experiment is less than 100 MB,whereas the data collected by a high-speed camera at the same frame rate exceeds 1 GB.Action data streams of the springs and cam were obtained and images were reconstructed from the event streams.The Lucas-Kanade optical flow algorithm and the normalised cross-correlation algorithm are applied to calculate the parameters of spring deformation characteristics and cam rotation characteristics for mechanical feature detection of HVCBs.This is the first attempt to utilize brain-inspired hardware technology for the status monitoring of electrical equipment.The advantages of dynamic vision sensing technology,such as high dynamic range,low data transmission,and low energy con-sumption,also offer significant benefits for air discharge monitoring and status moni-toring of electrical equipment.展开更多
基金supported by National Natural Science Foundation of China(Grant No.52475155)National Science Foundation for Hunan Province,China(Grant No.2023JJ30137)+2 种基金Guangdong Basic and Applied Basic Research Foundation(Grant No.2024A1515010684)Guangdong Basic and Applied Basic Research Foundation(Grant No.2023A1515240059)Program sponsored by the Foundation of Yuelushan Center for Industrial Innovation(Grant No.2023YCII0138).
文摘Pore formation is a significant challenges in the advancement of laser additive manufacturing(LAM)technologies.To address this issue,image data-driven pore detection techniques have become a research focus.However,existing methods are constrained by reliance on a single detection environment(e.g.,consistent brightness)and fixed input image sizes,limiting their predictive accuracy and application scope.This paper introduces an in-novative a pore detection method based on a deep learning model for laser-directed energy deposition(L-DED).The proposed method leverages the deep learning model’s ability to extract feature information from melt pool images captured by a high-speed camera,enabling efficient pore detection under varying brightness conditions and diverse image sizes.The detection results demonstrate that,under varying brightness levels,the proposed model achieves a pore detection accuracy of approximately 93.5% and a root mean square error(RMSE)of 0.42 for local porosity prediction.Additionally,even with changes in input image size,the model maintains robust performance,achieving a detection accuracy of 96% for pore status detection and an RMSE value of 0.09 for local porosity prediction.This study not only addresses the limitations of traditional detection techniques but also broadens the scope of online detection technologies.It highlights the potential of deep learning in complex industrial settings and provides valuable insights for advancing defect detection research in related fields.
基金supported by the National Natural Science Foundation of China(Nos.U19A20105,51837009,51807167,51922090,U1966602 and 52077182)the Scientific and Technological Funds for Young Scientists of Sichuan(No.2019JDJQ0019)。
文摘As the unique power entrance,the pantograph-catenary electrical contact system maintains the efficiency and reliability of power transmission for the high-speed train.Along with the fast development of high-speed railways all over the world,some commercialized lines are built for covering the remote places under harsh environment,especially in China;these environmental elements including wind,sand,rain,thunder,ice and snow need to be considered during the design of the pantograph-catenary system.The pantograph-catenary system includes the pantograph,the contact wire and the interface—pantograph slide.As the key component,this pantograph slide plays a critical role in reliable power transmission under dynamic condition.The fundamental material characteristics of the pantograph slide and contact wire such as electrical conductivity,impact resistance,wear resistance,etc.,directly determine the sliding electrical contact performance of the pantograph-catenary system;meanwhile,different detection methods of the pantograph-catenary system are crucial for the reliability of service and maintenance.In addition,the challenges brought from extreme operational conditions are discussed,taking the Sichuan-Tibet Railway currently under construction as a special example with the high-altitude climate.The outlook for developing the ultra-high-speed train equipped with the novel pantograph-catenary system which can address the harsher operational environment is also involved.This paper has provided a comprehensive review of the high-speed railway pantograph-catenary systems,including its progress,challenges,outlooks in the history and future.
基金This project was supported by National Natural Science Foundation of China(Grant No.31701318)National Natural Science Foundation of China Projects of International Cooperation and Exchanges(Grant No.31811540396)Basic Research Project of Education Department of Liaoning Province(Grant No.LSNJC201916).
文摘In this study,newly harvested and aged rice seeds were analyzed to determine their aging process,identify the difference between artificially and naturally aged seeds,and develop a rapid,accurate,and non-destructive detection method for water status and water distribution of rice seed with different vigor.To this end,an artificially accelerated aging test was conducted on the newly harvested rice seeds.Then,low-field nuclear magnetic resonance(LF-NMR)technology was applied to test the new(Shennong No.9816,2018),old(Shennong No.9816,2017),and artificially aged seeds(Shennong No.9816,2018).A standard germination test was conducted for three types of seeds.Finally,the differences of water status and distribution between rice seeds of different vigor were analyzed based on the standard germination test results and wave spectrometry information collected using LF-NMR.The results indicated that new seeds,old seeds,and the artificially accelerated aging rice seeds all exhibited two water phases,and the vigor of rice seeds after the artificial accelerated aging test was lower than that of new seeds.There were significant differences between the frequencies of bound water at the time of the peak and the time at the end of the peak for the three types of seeds.The two times showed an increasing trend for rice seeds with poor vigor,indicating that the ability of the water in the rice seeds having poor vigor to combine with other substances was weakened.There were significant differences between the distributions of free water peak end time for the three types of seeds.All the rice seeds with poor vigor exhibited a decreasing trend at this time,indicating that the freedom of free water inside the rice seed samples with poor vigor was weakened.The total water content of the artificially aged seeds and the aged seeds was higher than that of the new seeds,but the free water content increased from artificially aged seeds to new seeds to aged seeds.This indicates that LF-NMR technology is an effective detection method that can simply compare the differences in seed vitality with respect to water distribution as well as differentiate the seed internal water content of artificially aged and naturally aged seeds.
基金National Natural Science Foundation of China,Grant/Award Numbers:52077118,62411560155Guangdong Basic and Applied Basic Research Foundation,Grant/Award Number:2024A1515012597。
文摘Diagnosing the operational status of High-voltage circuit breakers(HVCBs)is crucial for ensuring the safe and stable operation of the grid.Mechanical characteristic parameters are effective indicators for evaluating the performance of HVCBs.Recent studies have shown that the actions of the springs and cams in HVCBs can be used to detect the operational status of the mechanical mechanisms,which occur extremely quickly,usually in the speed of m/ms.In this paper,dynamic vision sensing technology was employed to rapidly and dynamically capture the movements of the springs and cam of the HPL245B1 HVCB.The data volume of a single experiment is less than 100 MB,whereas the data collected by a high-speed camera at the same frame rate exceeds 1 GB.Action data streams of the springs and cam were obtained and images were reconstructed from the event streams.The Lucas-Kanade optical flow algorithm and the normalised cross-correlation algorithm are applied to calculate the parameters of spring deformation characteristics and cam rotation characteristics for mechanical feature detection of HVCBs.This is the first attempt to utilize brain-inspired hardware technology for the status monitoring of electrical equipment.The advantages of dynamic vision sensing technology,such as high dynamic range,low data transmission,and low energy con-sumption,also offer significant benefits for air discharge monitoring and status moni-toring of electrical equipment.
