摘要
为解决井下风速传感器获得的风速异常数据确定故障源位置问题,采用RBF神经网络方法确定可能引起各分支风速异常的分支集合,即建立通风系统故障巷道范围库,再通过对分支的灵敏度进行排序来选择故障巷道诊断的优先级.研究结果表明:RBF神经网络被训练好后,就可以不用建立具体的数学模型,得到整个网络各分支风量与风阻之间的关系.
In order to solve the problem of getting abnormal air velocity data by using air velocity transducer to determine the fault source location, the branch collection that may cause the abnormal air velocity is determined by using the RBF neural network method, and the ventilation system fault roadways scope library is established. Then the priority level of fault roadway diagnosis is selected through sorting branch sensitivity. The results show that specific mathematical models do not need to be established by using the trained RBF neural network, which will be able to get the relationship between roadway air volume changes and lead to its changes each branch drag.
出处
《辽宁工程技术大学学报（自然科学版）》
CAS
北大核心
2013年第6期749-753,共5页
Journal of Liaoning Technical University (Natural Science)
基金
国家自然科学基金资助项目(51204088)
关键词
矿山安全
故障源
诊断
RBF
灵敏度
传感器
分支
风阻
mine safety
fault source
diagnosis
RBF
sens!tivity
transducer
branch
air drag
