摘要
利用分类预测、聚类分析、关联规则、图形建模、智能预测等多种经典数据挖掘算法,建立车载CIR设备在故障、障碍、出入库良好率、返修率、高风险项点等方面大数据分析模型,提前发现CIR设备潜在隐患,智能推理,有效改善设备质量策略,优化设备维护手段。
By using classic data mining algorithms,such as classification prediction,clustering analysis,association rules,graphic modeling and intelligent forecasting and so on,a model of big data analysis for fault,obstacle,detection pass rate,rework rate and high risk items is built,which can detect potential risks in CIR equipment and give effective strategies to improve the quality of equipment by intelligent reasoning and to optimize the means of equipment maintenance.
出处
《铁道通信信号》
2017年第8期82-84,87,共4页
Railway Signalling & Communication
关键词
机车综合无线通信设备
数据挖掘
设备质量
大数据分析
Cab integrated radio communication equipment（CIR）
Data mining
Equipment Quality
Big data analysis
作者简介
吴宇：北京铁路局电务处 工程师 100036北京
