OPTIMIZATION OF THE COMPOSITION OF THE ELECTRICAL CONDUCTING LUBRICATING COMPOSITION FOR OPERATING APPLICATION IN THE BATTERY-BRUSH ASSEMBLY OF THE ELECTRIC MOTOR
( Pp. 16-20)

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Korotkov Vyacheslav Mikhailovich kandidat tehnicheskih nauk; docent kafedry «Tyagovyy podvizhnoy sostav»
Rostov State University of railway transport
Abstract:
A large number of methods for improving the efficiency of collector-brush units, a common feature of which is the creation of thin transition layers on the contact surface of materials that do not affect the current flow in contact and reduce the intensity of mechanical wear, have been studied. The data obtained in the course of the work showed that the electrically conductive lubricant composition helps to reduce the coefficient of friction to increase the wear resistance of the brush material and improve the switching conditions of the entire collector-brush unit. Considered the issue of optimization of the composition of the electrically conductive lubricating composition to the collector-brush Assembly of a traction motor. Providing with minimal costs the required composition of the conductive lubricant composition that meets the operating conditions is a difficult task and depends on a number of parameters of the components used. By using the generalized parameter of optimization of the desirability function of E. Harrington the optimal chemical composition of the conductive lubricant composition is obtained. Application of method E. Harrington to optimize the composition of the lubricant composition, allowed to meet the antagonistic requirements for its composition by mechanical and electrical characteristics, which had a positive impact on improving the performance and reliability of not only the collector-brush unit, but also the entire traction motor of the locomotive in operation.
How to Cite:
Korotkov V.M., (2019), OPTIMIZATION OF THE COMPOSITION OF THE ELECTRICAL CONDUCTING LUBRICATING COMPOSITION FOR OPERATING APPLICATION IN THE BATTERY-BRUSH ASSEMBLY OF THE ELECTRIC MOTOR. Computational Nanotechnology, 2 => 16-20.
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