Automated Self-trained System of Functional Control and State Detection of Railway Transport Nodes

• Autorzy: Akhmetov Bakhytzhan , Lakhno Valeriy , Oralbekova Ayaulym , Kaskatayev Zhanat , Mussayeva Gulmira

Identyfikatory

DOI

10.24425/ijet.2019.129804

• Języki publikacji: EN

Abstrakty

EN

Automation of data processing of contactless diagnostics (detection) of the technical condition of the majority of nodes and aggregates of railway transport (RWT) minimizes the damage from failures of these systems in operating modes. This becomes possible due to the rapid detection of serious defects at the stage of their origin. Basically, in practice, the control of the technical condition of the nodes and aggregates of the RWT is carried out during scheduled repairs. It is not always possible to identify incipient defects. Consequently, it is not always possible to warn personnel (machinists, repairmen, etc.) of significant damage to the RWT systems until their complete failure. The difficulties of obtaining diagnostic information is that there is interdependence between the main nodes of the RWT. This means that if physical damage occurs at any of the RWT nodes, in other nodes there can also occur malfunctions. As the main way to improve the efficiency of state detection of the nodes and aggregates of RWT, we see the direction of giving the adaptability property for an automated data processing system from various contactless diagnostic information removal systems. The global purpose can be achieved, in particular, through the use of machine learning methods and failure recognition (recognition objects). In order to improve the operational reliability and service life of the main nodes and aggregates of RWT, there are proposed an appropriate model and algorithm of machine learning of the operator control system of nodes and aggregates. It is proposed to use the Shannon normalized entropy measure and the Kullback-Leibler distance information criterion as a criterion of the learning effectiveness of the automated detection system and operator node state control of RWT. The article describes the application of the proposed method on the example of an automatic detection system (ADS) of the state of a traction motor of an electric locomotive. There are given the test data of the model and algorithm in the MATLAB environment.

Słowa kluczowe

EN

information intellectual technology of failure detection; functional control; learning matrix; learning algorithm; operator effectiveness criterion; nodes and aggregates; railway transport

• Wydawca: Polish Academy of Sciences, Committee of Electronics and Telecommunication
• Czasopismo: International Journal of Electronics and Telecommunications
• Rocznik: 2019
• Tom: Vol. 65, No. 3
• Strony: 491--496
• Opis fizyczny: Bibliogr. 19 poz., rys.

Twórcy

autor

Akhmetov Bakhytzhan

• Kazakh University Ways of Communications, Almaty, Kazakhstan

autor

Lakhno Valeriy

• National University of Life and Environmental Sciences of Ukraine, Kyiv

autor

Oralbekova Ayaulym

• Kazakh University Ways of Communications, Almaty, Kazakhstan

autor

Kaskatayev Zhanat

• Kazakh University Ways of Communications, Almaty, Kazakhstan

autor

Mussayeva Gulmira

• Kazakh University Ways of Communications, Almaty, Kazakhstan

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