关键词: 故障诊断, 动车组, 关联规则, 并行频繁模式增长算法, 局部频繁模式树, MapReduce

Abstract: Aiming at the knowledge mining problem of Electric Multiple Units (EMU) trains historical operation and maintenance data,the existing parallel frequent pattern growth algorithm and its disadvantages were analyzed from the perspective of guiding EMU's fault diagnosis.According to the requirements of EMU's fault diagnosis,an improved algorithm for data mining which used local frequent pattern tree instead of global frequent pattern tree was proposed.In this algorithm,the parallel processing in every data processing steps was adopted,the production rules of local frequent pattern tree were optimized,and the search strategy of frequent patterns were also improved.The efficiency in the process of mining association rules was greatly improved by the proposed algorithm,the relationship between fault information and state information was kept well by the mining results,and the invalid rules was removed reasonably.Based on analysis and experimental tests,the characteristics of fast speed,high efficiency and accuracy of the proposed algorithm in the process of knowledge acquisition of EMU's fault diagnosis was illustrated.

Key words: fault diagnosis, electric multiple units, association rule, parallel frequent pattern growth algorithm, local frequent pattern tree, MapReduce