Predictive maintenance is essential for the implementation of an innovative and efficient structural health monitoring strategy.Models capable of accurately interpreting new data automatically collected by suitably pl...Predictive maintenance is essential for the implementation of an innovative and efficient structural health monitoring strategy.Models capable of accurately interpreting new data automatically collected by suitably placed sensors to assess the state of the infrastructure represent a fundamental step,particularly for the railway sector,whose safe and continuous operation plays a strategic role in the well-being and development of nations.In this scenario,the benefits of a digital twin of a bonded insu-lated rail joint(IRJ)with the predictive capabilities of advanced classification algorithms based on artificial intelligence have been explored.The digital model provides an accurate mechanical response of the infrastructure as a pair of wheels passes over the joint.As bolt preload conditions vary,four structural health classes were identified for the joint.Two parameters,i.e.gap value and vertical displacement,which are strongly correlated with bolt preload,are used in different combinations to train and test five predictive classifiers.Their classification effectiveness was assessed using several performance indica-tors.Finally,we compared the IRJ condition predictions of two trained classifiers with the available data,confirming their high accuracy.The approach presented provides an interesting solution for future predictive tools in SHM especially in the case of complex systems such as railways where the vehicle-infrastructure interaction is complex and always time varying.展开更多
A two-step information extraction method is presented to capture the specific index-related information more accurately.In the first step,the overall process variables are separated into two sets based on Pearson corr...A two-step information extraction method is presented to capture the specific index-related information more accurately.In the first step,the overall process variables are separated into two sets based on Pearson correlation coefficient.One is process variables strongly related to the specific index and the other is process variables weakly related to the specific index.Through performing principal component analysis(PCA)on the two sets,the directions of latent variables have changed.In other words,the correlation between latent variables in the set with strong correlation and the specific index may become weaker.Meanwhile,the correlation between latent variables in the set with weak correlation and the specific index may be enhanced.In the second step,the two sets are further divided into a subset strongly related to the specific index and a subset weakly related to the specific index from the perspective of latent variables using Pearson correlation coefficient,respectively.Two subsets strongly related to the specific index form a new subspace related to the specific index.Then,a hybrid monitoring strategy based on predicted specific index using partial least squares(PLS)and T2statistics-based method is proposed for specific index-related process monitoring using comprehensive information.Predicted specific index reflects real-time information for the specific index.T2statistics are used to monitor specific index-related information.Finally,the proposed method is applied to Tennessee Eastman(TE).The results indicate the effectiveness of the proposed method.展开更多
Colorado potato beetle(CPB)is one of the most devastating invasive insects and it is native to North America.It feeds on several wild species of the genus Solamum,such as S.elaeagnifolium and S.rostratum Dunal,and is ...Colorado potato beetle(CPB)is one of the most devastating invasive insects and it is native to North America.It feeds on several wild species of the genus Solamum,such as S.elaeagnifolium and S.rostratum Dunal,and is one of the major pests of potato and eggplant.Beginning in the early 19 th century,CPB has rapidly spread across North America,Europe,and Central Asia.CPB was first reported to invade Xinjiang of China in 1993 and it was effectively controlled in Mori County.Since 2013,CPB has also been found in Jilin and Heilongjiang in Northeast China,and it likely migrated to these provinces from Russia.Thus,China has become the frontier for the global CPB spread,and risk management and monitoring systems for this pest are urgently needed.Here,we summarize pest management methods that are used in areas at the frontier of the CPB invasion,and put forward frameworks for further preventing and controlling of the spread of CPB.The management methods for CPB can also serve as an example for the control of invasive species mitigation in frontier areas.展开更多
Diabetes is spreading at an alarming rate worldwide,becoming one of the most significant public health challenges[1].Effective diabetes management requires precise,dynamic,and personalized monitoring strategies to cap...Diabetes is spreading at an alarming rate worldwide,becoming one of the most significant public health challenges[1].Effective diabetes management requires precise,dynamic,and personalized monitoring strategies to capture the multi-system physiological responses triggered by factors such as diet,exercise,and emotional fluctuations[2,3].Traditional diabetes monitoring relies on fingerstick blood sampling to assess blood glucose levels,which not only requires frequent skin punctures to obtain blood samples but also has a certain degree of lag.展开更多
基金the National Recovery and Resilience Plan (NRRP), Mission 4 Component 2 Investment 1.4-Call for tender No. 3138 of 16/12/2021 of Italian Ministry of University and Research funded by the European Union-Next Generation EU. Award Number: Project code CN00000023Concession Decree No. 1033 of 17/06/2022 adopted by the Italian Ministry of University and Research, CUP D93C22000400001, “Sustainable Mobility Center” (CNMS). Spoke 4-Rail Transportation
文摘Predictive maintenance is essential for the implementation of an innovative and efficient structural health monitoring strategy.Models capable of accurately interpreting new data automatically collected by suitably placed sensors to assess the state of the infrastructure represent a fundamental step,particularly for the railway sector,whose safe and continuous operation plays a strategic role in the well-being and development of nations.In this scenario,the benefits of a digital twin of a bonded insu-lated rail joint(IRJ)with the predictive capabilities of advanced classification algorithms based on artificial intelligence have been explored.The digital model provides an accurate mechanical response of the infrastructure as a pair of wheels passes over the joint.As bolt preload conditions vary,four structural health classes were identified for the joint.Two parameters,i.e.gap value and vertical displacement,which are strongly correlated with bolt preload,are used in different combinations to train and test five predictive classifiers.Their classification effectiveness was assessed using several performance indica-tors.Finally,we compared the IRJ condition predictions of two trained classifiers with the available data,confirming their high accuracy.The approach presented provides an interesting solution for future predictive tools in SHM especially in the case of complex systems such as railways where the vehicle-infrastructure interaction is complex and always time varying.
基金Projects(61374140,61673173)supported by the National Natural Science Foundation of ChinaProjects(222201717006,222201714031)supported by the Fundamental Research Funds for the Central Universities,China
文摘A two-step information extraction method is presented to capture the specific index-related information more accurately.In the first step,the overall process variables are separated into two sets based on Pearson correlation coefficient.One is process variables strongly related to the specific index and the other is process variables weakly related to the specific index.Through performing principal component analysis(PCA)on the two sets,the directions of latent variables have changed.In other words,the correlation between latent variables in the set with strong correlation and the specific index may become weaker.Meanwhile,the correlation between latent variables in the set with weak correlation and the specific index may be enhanced.In the second step,the two sets are further divided into a subset strongly related to the specific index and a subset weakly related to the specific index from the perspective of latent variables using Pearson correlation coefficient,respectively.Two subsets strongly related to the specific index form a new subspace related to the specific index.Then,a hybrid monitoring strategy based on predicted specific index using partial least squares(PLS)and T2statistics-based method is proposed for specific index-related process monitoring using comprehensive information.Predicted specific index reflects real-time information for the specific index.T2statistics are used to monitor specific index-related information.Finally,the proposed method is applied to Tennessee Eastman(TE).The results indicate the effectiveness of the proposed method.
基金supported by the Basic Scientific Funding from Chinese Academy of Inspection and Quarantine (2017JK038 and 2014JK014)
文摘Colorado potato beetle(CPB)is one of the most devastating invasive insects and it is native to North America.It feeds on several wild species of the genus Solamum,such as S.elaeagnifolium and S.rostratum Dunal,and is one of the major pests of potato and eggplant.Beginning in the early 19 th century,CPB has rapidly spread across North America,Europe,and Central Asia.CPB was first reported to invade Xinjiang of China in 1993 and it was effectively controlled in Mori County.Since 2013,CPB has also been found in Jilin and Heilongjiang in Northeast China,and it likely migrated to these provinces from Russia.Thus,China has become the frontier for the global CPB spread,and risk management and monitoring systems for this pest are urgently needed.Here,we summarize pest management methods that are used in areas at the frontier of the CPB invasion,and put forward frameworks for further preventing and controlling of the spread of CPB.The management methods for CPB can also serve as an example for the control of invasive species mitigation in frontier areas.
文摘Diabetes is spreading at an alarming rate worldwide,becoming one of the most significant public health challenges[1].Effective diabetes management requires precise,dynamic,and personalized monitoring strategies to capture the multi-system physiological responses triggered by factors such as diet,exercise,and emotional fluctuations[2,3].Traditional diabetes monitoring relies on fingerstick blood sampling to assess blood glucose levels,which not only requires frequent skin punctures to obtain blood samples but also has a certain degree of lag.
