At present, the research invested by the railway department in the development of railway transportation is very huge. The railway transportation related technologies are constantly updated, and the equipment automation and intelligent level are constantly developing, which puts forward higher requirements for railway monitoring. Railway is an indispensable part of railway transportation. Railway signal equipment can reflect the real situation of railway in real time and carry out emergency treatment for the situation of railway, which ensures the safety on the railway transportation road. Therefore, higher standards and requirements should be put forward for the intelligent monitoring system of railway signal. This paper mainly studies the railway signal intelligent monitoring system based on data mining. This paper expounds the design principles of the railway signal intelligent monitoring system to standardize the design of the system, then designs the railway signal intelligent monitoring system, and then expounds the database design, put the collected railway signal equipment data into the database for comparative research, so as to realize fault diagnosis. This paper studies the railway signal intelligent monitoring system in collecting railway signal data samples, comparing them in the database using data mining technology, and comparing their acquaintance degree, so as to understand the actual operation of the monitoring system. The research results show that in the test and research of railway signal intelligent monitoring system, there is little difference in the monitoring time of samples. The average collection time of railway signal samples is about 4 seconds. The longer the comparison time, the higher the similarity between the samples and fault manifestation data. For example, the comparison time of sample 5 occupies 8.93 seconds, and its similarity estimation index is 13.91.


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    Titel :

    Railway Signal Intelligent Monitoring System Based on Data Mining


    Beteiligte:
    Wu, Jianjun (Autor:in)


    Erscheinungsdatum :

    10.12.2021


    Format / Umfang :

    380348 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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