铁道科学与工程学报2017，Vol.14Issue(9)：1983-1989,7.
基于模糊认知图模型的轨道电路故障诊断
Track circuit fault diagnosis based on fuzzy cognitive map model
摘要
Abstract
In view of the disadvantages of low diagnostic efficiency, long cycle time of diagnosis and high dependence on the experiences of data analyzers of ZPW-2000A track circuit fault distinguishes with manual work according to monitoring data, the concept of Fuzzy Cognitive Map was introduced into fault diagnosis for ZPW-2000A track circuit. Based on the mechanism analysis of ten kinds of faults with the combination of the present day experiences, and the analysis of different types of faults data to obtain faults feature parameters, the Fuzzy Cognitive Map classifier based on fault types and feature parameters were establish, and to complete FCM weights learning in terms of the adaptive genetic algorithm during the process. Computer simulations showed that the monitoring based FCMCM diagnosis method proposed in this paper for ZPW-2000A track circuit is effective and feasible, and was compared with manual analysis monitoring data to discriminate failure and was found to be a high failure recognizer within a short diagnosis time.关键词
轨道电路/故障诊断/模糊认知图模型/自适应遗传算法Key words
track circuit/fault diagnosis/fuzzy cognitive map model/adaptive genetic algorithm分类
交通工程引用本文复制引用
陈星,董昱..基于模糊认知图模型的轨道电路故障诊断[J].铁道科学与工程学报,2017,14(9):1983-1989,7.基金项目
国家自然科学基金资助项目(61164010) （61164010）
