{"title":"A Fault Precaution Model based on Fault Sequential Patterns for Metro Overhead Contact System","authors":"Yigu Liu, Long Yu, Kaiyi Qian","doi":"10.1109/ITSC.2019.8917277","DOIUrl":null,"url":null,"abstract":"Achieving accurate fault precaution of metro overhead contact system (OCS) is of great significance for the reliability of metro system. However, the current metro maintenance strategies generally adopt the idea of condition monitoring and fault maintenance, which cannot achieve real preventive maintenance. In this paper, we propose a fault precaution model for metro OCS which mines fault sequential patterns from historical fault data. As a case study, we analyzed the historical fault data of a city in south China and formed a fault relational network out of it. At last, detailed process of precaution suggestions generation is given based on the analysis results.","PeriodicalId":6717,"journal":{"name":"2019 IEEE Intelligent Transportation Systems Conference (ITSC)","volume":"48 1","pages":"394-398"},"PeriodicalIF":0.0000,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE Intelligent Transportation Systems Conference (ITSC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ITSC.2019.8917277","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Achieving accurate fault precaution of metro overhead contact system (OCS) is of great significance for the reliability of metro system. However, the current metro maintenance strategies generally adopt the idea of condition monitoring and fault maintenance, which cannot achieve real preventive maintenance. In this paper, we propose a fault precaution model for metro OCS which mines fault sequential patterns from historical fault data. As a case study, we analyzed the historical fault data of a city in south China and formed a fault relational network out of it. At last, detailed process of precaution suggestions generation is given based on the analysis results.