DEVELOPMENT OF ALGORITHMIC MODELS FOR RESEARCH OF RELIABILITY PARAMETERS OF TROLLEYBUS TRACTION ELECTRIC MOTORS IN THE OPERATION PROCESS

Vyacheslav Shavkun, Tatyana Pavlenko, Olha Kozlova

Abstract


The analysis of the reliability parameters of traction engines during the operation of trolleybuses in different modes is done. The presented groups of operational factors are lead to the emergence of censored samples. The methods of express analysis of engine reliability using technical diagnostic tools are determined. A block diagram of an algorithmic model for studying the reliability parameters of traction electric motors of trolleybuses during operation has been developed. It allows at any time to evaluate the influence of operational factors on the reliability indicators of traction electric engines and to more clearly and reasonably assign organizational, technical and preventive measures.

It has been established that the effective and reliable operation of urban electric transport, in particular trolleybuses, depends on the reliability of individual components and assemblies of rolling stock, as well as on the quality and timely control of their parameters during operation.

In practice, it has been proved that failure of traction electric motors (TEM) of trolleybuses leads to significant material losses at electric transport enterprises. And one of the main parameters of reliability of traction electric motors of trolleybuses is the reliability of operation during operation.

Based on the research results, practical recommendations are developed on the rational choice of diagnostic parameters. Their implementation at electric transport enterprises will increase the reliability of traction electric motors as a whole up to 10 %.

The developed methodology allows the study (assessment) of the reliability parameters of any type of traction engine of a trolley during operation

Keywords


traction electric motor; reliability; distribution law; statistical data; operational factors; mathematical model; calculation algorithm

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DOI: http://dx.doi.org/10.21303/2461-4262.2020.001174

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ISSN 2461-4262 (Online), ISSN 2461-4254 (Print)