With the increase of the scale and quantity of China’s EMU trains and the continuous improvement of the level of integrated intelligence, the application and maintenance system of EMU has accumulated a large number of fault warning information data. It is of great significance to reduce the scope of fault search and improve the efficiency of maintenance by using an efficient association rule mining algorithm to obtain the correlation knowledge among alarms from the data of fault alarm information. FP-Growth algorithm is one of the classical methods in association rule mining algorithm, which is used to mine frequent itemsets in the data set. To solve the problem of unequal importance proportion of fault alarm data items, a weight assigned FP-Growth algorithm was proposed, which combined the importance degree of fault alarm and calculated the support threshold value of transaction items, thus generating a conditional pattern base. The experimental results show that the weight assigned FP-Growth algorithm can effectively reduce the number of mining rules, and accelerate the algorithm running speed with the variation of support threshold, which will improve the efficiency of fault warning data mining of EMU.


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    Titel :

    Research on Alarm Correlation Analysis for EMU Train Based on Association Rule Mining Algorithm


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Qin, Yong (Herausgeber:in) / Jia, Limin (Herausgeber:in) / Liang, Jianying (Herausgeber:in) / Liu, Zhigang (Herausgeber:in) / Diao, Lijun (Herausgeber:in) / An, Min (Herausgeber:in) / Wang, Chong (Autor:in) / Wang, Lide (Autor:in) / Qiu, Yu (Autor:in) / Yang, Yueyi (Autor:in)

    Kongress:

    International Conference on Electrical and Information Technologies for Rail Transportation ; 2021 October 21, 2021 - October 23, 2021



    Erscheinungsdatum :

    2022-02-23


    Format / Umfang :

    8 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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