摘要
分析了全断面掘进机复杂的故障机理和运行参数,研究了将粗糙集和决策树应用到数据挖掘中的方法.以全断面掘进机刀盘的一些实时数据为例,采用MATLAB 7.0对数据进行离散化处理,结合粗糙集属性约简的算法对故障样本进行冗余属性的约简;然后,利用决策树算法对约简后的故障样本集进行规则提取,利用数据挖掘工具Clementine实现了C4.5算法和改进的C4.5算法,对其结果进行了对比分析;最后,运用VB编程对全断面掘进机采集的部分数据进行测试,结果表明该融合算法是一种快速、有效、可靠的故障检测与诊断的新途径.
Complex fault mechanism and operation parameters of the tunnel boring machine（ TBM）were analyzed,and the method of rough set and decision tree algorithm applying to data mining was studied. Take several MATLAB 7. 0 dispersed data of tunnel boring machine cutter head as an example,the redundancy attribute of fault samples was reduced by the combination with the rough set attribute reduction algorithm. The rules were extracted with the decision-making tree algorithm.The C4. 5 algorithm and the improved C4. 5 algorithm were implemented with the data mining tool Clementine,with the results compared. The data was tested by the VB programming. The results showed that the fusion algorithm is a rapid,effective and reliable approach for fault detection and diagnosis.
出处
《东北大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2015年第4期527-531,541,共6页
Journal of Northeastern University(Natural Science)
基金
国家重点基础研究发展计划项目(2010CB736007)
中央高校基本科研业务费专项资金资助项目(N110603007)
关键词
全断面掘进机
数据挖掘
粗糙集
决策树
融合算法
tunnel boring machine
data mining
rough set
decision tree
fusion algorithm
