公开(公告)号：US20160282229A1

公开(公告)日：2016-09-29

申请号：US14890167

申请日：2014-12-05

申请人： BEIJING JIAOTONG UNIVERSITY

发明人： Yong QIN ,  Limin JIA ,  Jingxuan SHI ,  Xiaoqing CHENG ,  Yuan ZHANG ,  Shan YU ,  Linlin KOU ,  Zhenyu ZHANG

IPC分类号： G01M17/10

CPC分类号： G01M17/10 ,  G01M17/08 ,  G06N3/02

摘要： The present invention provides a fault prediction and condition-based repair method of an urban rail train bogie. An optimum service life distribution model of a framework, a spring device, a connecting device, a wheel set and axle box, a driving mechanism, and a basic brake device of a bogie is determined by adopting a method based on survival analysis; a reliability characteristic function of each subsystem is obtained; then, a failure rate of each subsystem of the bogie is calculated by adopting a neural network model optimized by an evolutionary algorithm; and finally, proportional risk modelling is conducted by taking the failure rate and safe operation days of each subsystem of the bogie as concomitant variables; and on the basis of cost optimization, thresholds and control limits for condition-based repair of a bogie system are obtained.

摘要翻译： 本发明提供了一种城市轨道车辆转向架的故障预测和基于状态的修复方法。 通过采用基于生存分析的方法确定框架，弹簧装置，连接装置，轮组和轴箱，驱动机构和转向架的基本制动装置的最佳使用寿命分布模型; 获得每个子系统的可靠性特征函数; 然后，通过采用由进化算法优化的神经网络模型计算转向架的每个子系统的故障率; 最后，通过将转向架每个子系统的故障率和安全运行时间作为伴随变量进行比例风险建模; 并在成本优化的基础上，获得了转向架系统基于状态修复的阈值和控制限制。

公开(公告)号：US20170015339A1

公开(公告)日：2017-01-19

申请号：US15123684

申请日：2015-11-27

申请人： BEIJING JIAOTONG UNIVERSITY

发明人： Limin JIA ,  Yong QIN ,  Yanhui WANG ,  Shuai LIN ,  Hao SHI ,  Lifeng BI ,  Lei GUO ,  Lijie LI ,  Man LI

IPC分类号： B61L99/00 ,  G06N99/00 ,  H04L29/08

CPC分类号： B61L99/00 ,  B61L27/0055 ,  B61L27/0083 ,  G06F17/50 ,  G06N99/005 ,  H04L67/12

摘要： The invention discloses a complex network-based high speed train system safety evaluation method. The method includes steps as follows: (1) constructing a network model of a physical structure of a high speed train system, and constructing a functional attribute degree of a node based on the network model; (2) extracting a functional attribute degree, a failure rate and mean time between failures of a component as an input quantity, conducting an SVM training using LIBSVM software; (3) conducting a weighted kNN-SVM judgment: an unclassifiable sample point is judged so as to obtain a safety level of the high speed train system. For a high speed train system having a complicated physical structure and operation conditions, the method can evaluate the degree of influences on system safety when a state of a component in the system changes. The experimental result shows that the algorithm has high accuracy and good practicality.

摘要翻译： 本发明公开了一种基于复杂网络的高速列车系统安全评估方法。 该方法包括以下步骤：（1）构建高速列车系统物理结构的网络模型，并根据网络模型构建节点的功能属性度; （2）提取组件故障之间的功能属性度，故障率和平均时间作为输入量，使用LIBSVM软件进行SVM训练; （3）进行加权kNN-SVM判断：判断为不可分类的采样点，以获得高速列车系统的安全等级。 对于具有复杂的物理结构和操作条件的高速列车系统，当系统中的部件的状态改变时，该方法可以评估对系统安全性的影响程度。 实验结果表明该算法精度高，实用性好。