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    动车组运维效率关联规则挖掘优化算法

    Optimization Algorithm of Association Rule Mining for EMU Operation and Maintenance Efficiency

    • 摘要: 随着动车组运营时间和运营里程的增长,动车组运维系统积累了大量的数据.利用高效的关联规则挖掘算法从动车组运维数据中快速发现有用的信息,对于提高动车组关键部件运维效率具有重要意义.针对动车组运维数据的数据量巨大、价值密度低的特点,设计一种基于近似最小完美Hash函数的AMPHP(approximate minimum perfect hashing and pruning)算法,相较于传统的直接Hash和修剪(direct hashing and pruning, DHP)算法,它可以过滤掉所有的非频繁项集,无需额外的数据库扫描.为了突破单机算法的性能限制,借鉴SON算法思想对AMPHP算法进行并行化改进,提出AMPHP-SON算法,进一步提高算法性能.使用实际的动车组牵引电机运维数据进行测试分析,实验结果表明,AMPHP-SON算法具有很好的时间性能,且挖掘出的规则可以有效地指导动车组修程修制优化,从而达到提高动车组运维效率的目的.

       

      Abstract: With the increase of EMU operation time and mileage, EMU operation and maintenance system has accumulated a large amount of data. Using the high-performance association rule mining algorithms to quickly find useful information from the EMU operation and maintenance data, is of significant importance for improving the operation and maintenance efficiency of the key components of the EMU. In the view of the characteristics of EMU operation and maintenance data—huge volume and low value density, we design the AMPHP algorithm based on the approximate minimal perfect Hash function. Compared with the traditional DHP algorithm, it can filter out all the infrequent item sets without additional database scanning. In order to break the limitation of the single machine algorithm and further improve the performance of the algorithm, we use the idea of SON algorithm for reference to parallelize the AMPHP algorithm and finally propose the AMPHP-SON algorithm. Some experiments have been performed on the operation and maintenance data of EMU traction motor. The experimental result shows that the AMPHP-SON algorithm has good time performance and the rules dug out can be effectively used to guide the optimization of the repair class and repair system of EMU, so as to improve the efficiency of EMU operation and maintenance.

       

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