一种基于改进FP-Growth算法的动车组故障预测研究

Fault prediction of EMU based on improved FP-Growth algorithm

动车组的故障预测和健康管理是目前的研究热点,其中,故障预测的关键是寻找动车组故障信息和状态信息之间的关联关系。频繁模式增长(FP-Growth)算法是关联规则挖掘中的经典算法之一,用来挖掘频繁项集。针对动车组故障数据提出了一种改进的FP-Growth(IFP-Growth,Improved FPGrowth)算法,采用先序遍历FP-tree的方法产生条件模式基。实验结果表明,IFP-Growth算法能够有效提高动车组故障数据挖掘的效率,并且能够有效地挖掘动车组故障信息和状态信息之间的关联关系。

Prognostics and Health Management(PHM)of EMU is the hotspot of current research.The key of fault prediction is to find the relation between fault information and status information of EMU.The FP-Growth algorithm is one of the classical algorithms in association rule mining.It is used to excavate frequent item sets.This paper proposed an improved FP-Growth(IFP-Growth)algorithm for EMU fault data.It adopted pre-traversing FP-tree to generate conditional pattern bases.The experimental results showed that the IFP-Growth algorithm could effectively improve the efficiency of data mining of EMU faults and find the relation between fault information and status information of EMU.