Fault frequency of catenary is related to meteo-rological conditions. In this work, based on the historical data, catenary fault frequency and weather-related fault rate are introduced to analyse the correlation betwe...Fault frequency of catenary is related to meteo-rological conditions. In this work, based on the historical data, catenary fault frequency and weather-related fault rate are introduced to analyse the correlation between catenary faults and meteorological conditions, and further the effect of meteorological conditions on catenary oper-ation. Moreover, machine learning is used for catenary fault prediction. As with the single decision tree, only a small number of training samples can be classified cor-rectly by each weak classifier, the AdaBoost algorithm is adopted to adjust the weights of misclassified samples and weak classifiers, and train multiple weak classifiers. Finally, the weak classifiers are combined to construct a strong classifier, with which the final prediction result is obtained. In order to validate the prediction method, an example is provided based on the historical data from a railway bureau of China. The result shows that the mapping relation between meteorological conditions and catenary faults can be established accurately by AdaBoost algorithm. The AdaBoost algorithm can accurately predict a catenary fault if the meteorological conditions are provided.展开更多
Reasons for substution of Al and/or Si by P in TRIP steel were described.The details in thermodynamic and kinetic analyses and model parametre for the estimation,as well as the calculation results were listed.Low temp...Reasons for substution of Al and/or Si by P in TRIP steel were described.The details in thermodynamic and kinetic analyses and model parametre for the estimation,as well as the calculation results were listed.Low temperature tests were also shown to support the calculation result.Strengthening mechanism for different composition high Mn steels was discussed based on the optical micrograph,XRD,TEM and SEM measurements.It was therefore realized stack fault and its effect in phase transformation in high Mn steel was in the dependence of composition,strain and heat treatment.展开更多
基金supported by the Scientific and Technological Research and Development Program of China Railway Corporation under Grant N2018G023by the Science and Technology Projects of Sichuan Province under Grants 2018RZ0075
文摘Fault frequency of catenary is related to meteo-rological conditions. In this work, based on the historical data, catenary fault frequency and weather-related fault rate are introduced to analyse the correlation between catenary faults and meteorological conditions, and further the effect of meteorological conditions on catenary oper-ation. Moreover, machine learning is used for catenary fault prediction. As with the single decision tree, only a small number of training samples can be classified cor-rectly by each weak classifier, the AdaBoost algorithm is adopted to adjust the weights of misclassified samples and weak classifiers, and train multiple weak classifiers. Finally, the weak classifiers are combined to construct a strong classifier, with which the final prediction result is obtained. In order to validate the prediction method, an example is provided based on the historical data from a railway bureau of China. The result shows that the mapping relation between meteorological conditions and catenary faults can be established accurately by AdaBoost algorithm. The AdaBoost algorithm can accurately predict a catenary fault if the meteorological conditions are provided.
文摘Reasons for substution of Al and/or Si by P in TRIP steel were described.The details in thermodynamic and kinetic analyses and model parametre for the estimation,as well as the calculation results were listed.Low temperature tests were also shown to support the calculation result.Strengthening mechanism for different composition high Mn steels was discussed based on the optical micrograph,XRD,TEM and SEM measurements.It was therefore realized stack fault and its effect in phase transformation in high Mn steel was in the dependence of composition,strain and heat treatment.
