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
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.
文摘近年来,列车以太网以其高带宽、低成本、通用性强、组网灵活等优点成为国内外的研究热点,已批量应用于国内新型动车组、城际列车、市域列车及地铁列车。然而,要实现以太网数据的收发功能,需要在既有CPU板卡上增加TRDP(train real-time data protocol)以太网卡。增加此网卡后,虽然可以解决TRDP数据包的收发问题,但是,一方面会增加数据传输节点数量,成为潜在故障节点,另一方面会增加制造及维护成本。针对上述问题,文章提出了一种基于既有CPU板卡设计的TRDP数据处理方法,此方法已在多个城市轨道交通项目中得到应用,验证了其通用性和兼容性,并证明能够普遍适用于单网口和双网口设备,支持各种列车总线拓扑结构。此外,该方案还具有配置灵活、通信稳定可靠的特点。
