动车组故障预测与健康管理(PHM,Prognostics and Health Management)模型研究工作围绕动车组运维数据开展。数据是动车组PHM模型的驱动力,数据计算是动车组PHM模型的核心。文章从动车组PHM模型应用现状出发,对动车组PHM模型数据架构进...动车组故障预测与健康管理(PHM,Prognostics and Health Management)模型研究工作围绕动车组运维数据开展。数据是动车组PHM模型的驱动力,数据计算是动车组PHM模型的核心。文章从动车组PHM模型应用现状出发,对动车组PHM模型数据架构进行了优化设计,研究了动车组车载信息无线传输系统(WTDS,Wireless Transmission Device System)数据清洗及存储等关键技术,提升了PHM模型源数据处理效率。展开更多
Failure depth of coal seam floors is one of the important considerations that must be kept in mind when mining is carried out above a confined aquifer. In order to study the factors that affect the failure depth of co...Failure depth of coal seam floors is one of the important considerations that must be kept in mind when mining is carried out above a confined aquifer. In order to study the factors that affect the failure depth of coal seam floors such as mining depth, coal seam pitch, mining thickness, workface length and faults, we propose a combined artificial neural networks (ANN) prediction model for failure depth of coal seam floors on the basis of existing engineering data by using genetic algorithms to train the ANN. A practical engineering application at the Taoyuan Coal Mine indicates that this method can effectively determine the network struc- ture and training parameters, with the predicted results agreeing with practical measurements. Therefore, this method can be applied to relevant engineering projects with satisfactory results.展开更多
文摘动车组故障预测与健康管理(PHM,Prognostics and Health Management)模型研究工作围绕动车组运维数据开展。数据是动车组PHM模型的驱动力,数据计算是动车组PHM模型的核心。文章从动车组PHM模型应用现状出发,对动车组PHM模型数据架构进行了优化设计,研究了动车组车载信息无线传输系统(WTDS,Wireless Transmission Device System)数据清洗及存储等关键技术,提升了PHM模型源数据处理效率。
基金Projects 50874103 supported by the National Natural Science Foundation of China2006CB202210 by the National Basic Research Program of China+1 种基金BK2008135 by the Natural Science Foundation of Jiangsu Provincethe Qing-lan Project of Jiangsu Province
文摘Failure depth of coal seam floors is one of the important considerations that must be kept in mind when mining is carried out above a confined aquifer. In order to study the factors that affect the failure depth of coal seam floors such as mining depth, coal seam pitch, mining thickness, workface length and faults, we propose a combined artificial neural networks (ANN) prediction model for failure depth of coal seam floors on the basis of existing engineering data by using genetic algorithms to train the ANN. A practical engineering application at the Taoyuan Coal Mine indicates that this method can effectively determine the network struc- ture and training parameters, with the predicted results agreeing with practical measurements. Therefore, this method can be applied to relevant engineering projects with satisfactory results.
