摘要
为识别接触网中关键故障零部件,本文提出了一种改进灰色关联度的接触网故障零部件预防模型。在改进的灰色关联度中,引入信息熵构建动态分辨系数,降低故障零部件数据库中异常数据的影响。与此同时,利用欧几里得集合贴近度计算故障零部件之间的关联度,以此表征数据序列波动时故障零部件之间的动态变化特性。最后,将提出的灰色关联度模型用于识别关键故障零部件。仿真结果表明,提出的模型计算简便,易于推广,为接触网维修策略提供了有价值的借鉴。
This paper presents an improved gray correlation degree model for the preventive identification of critical fault components in contact networks.This model incorporates information entropy to construct the dynamic resolution coefficients,mitigating the impact of abnormal data in the fault component database.Moreover,the Euclidean set proximity is utilized to calculate the correlation degree between fault components,to characterize the dynamic changes among fault components when the data sequence fluctuates.Finally,the proposed gray correlation degree model is applied to the identification of critical fault components.Simulation results demonstrate that the proposed model is computationally efficient,easily applicable to various scenarios,and provides valuable insights for devising contact network maintenance strategies.
作者
何宗伦
刘帝洋
韦晓广
韩正庆
He Zonglun;Liu Diyang;Wei Xiaoguang;Han Zhengqing(Key Laboratory of Railway Industry on Smart Traction Power Supply,Southwest Jiaotong University,Sichuan,Chengdu 611756,China;China Railway Design Corporation,Tianjin 300308,China;School of Electrical Engineering,Southwest Jiaotong University,Sichuan,Chengdu 611756,China)
出处
《铁道技术标准（中英文）》
2023年第8期1-6,共6页
Railway Technical Standard(Chinese & English)
基金
中国国家铁路集团有限公司科技研究开发计划(N2022G007)。
关键词
接触网
故障零部件
灰色关联度
contact network
fault components
gray correlation degree
