摘要
针对现有轨道电路实行故障维修和计划维修的缺陷,提出一种以状态为基础的设备故障预测维修机制。设计故障预测与健康管理(PHM)体系结构与工作流程,采用模糊神经网络的方法,建立一种高压不对称脉冲轨道电路故障预测模型。选定2个输入参数和4个故障输出参数,输出参数利用故障可信度描述,根据专家知识和现场经验形成模糊推理规则表。通过仿真实验验证了PHM体系结构是有效的。
The track circuit performs the strategy of fault and planned maintenance until now. In order to overcome the defect of this strategy, this paper presents a state-based mechanism for predictive maintenance of equipment, and designs a kind of architecture and workflow for Prognostics and Health Management（PHM） technology. A PHM system for High-voltage Asymmetric Pulse（HVAP） track circuit is designed based on fuzzy neural network, and two input parameters and four output typical faults are set in this system, the output faults are described with credibility, and the fuzzy inference rule table is gotten according to the expert knowledge and the experience from field. Computer simulation results show that the architecture of PHM is effective
出处
《计算机工程》
CAS
CSCD
2012年第20期232-235,共4页
Computer Engineering
基金
中央高校基本科研业务费专项基金资助项目(2009YJS014)
关键词
轨道电路
故障诊断
故障预测
健康管理
模糊神经网络
track circuit
fault diagnosis
fault prognostics
health management
fuzzy neural network
