The article deals with the problem of providing diagnostics, monitoring and control of the residual life of traction electric motors of electric rolling stock. The analysis shows that the existing diagnostic systems of traction engines do not allow us to carry out diagnostics to make a decision on maintaining their operability and monitoring the residual resource. Due to the fact that the main cause of breakdowns and inter-turn closures of the insulation of the TED is its overheating, it is proposed to improve the method of temperature control of the armature winding, additional poles, compensation winding. The article proposes a system with an algorithm for analyzing the condition, comparing the current operating modes of the traction engine of a 2ES6 series locomotive for further calculation of its residual life, monitoring and control of its condition. The system under development will collect data from diagnostic sensors, save them and send all the accumulated data to the server. Data processing is carried out on the server using artificial intelligence and machine learning methods and a decision is made on the technical condition and residual resource of the traction motor. As a result, it becomes possible to assess the critical condition of the traction motor.


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    Titel :

    Development of On-Board Systems of Predictive Diagnostics of Electric Rolling Stock Traction Motor


    Weitere Titelangaben:

    Lect. Notes in Networks, Syst.


    Beteiligte:
    Guda, Alexander (Herausgeber:in) / Pastukhov, Kirill (Autor:in) / Istomin, Stanislav (Autor:in) / Yrasov, Oleg (Autor:in)

    Kongress:

    International School on Neural Networks, Initiated by IIASS and EMFCSC ; 2022 ; St.Petersburg, Russia February 08, 2022 - February 10, 2022



    Erscheinungsdatum :

    16.11.2022


    Format / Umfang :

    10 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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