Abstract
The objective of the article is to determine the quality conditions of a locomotive traction motor frame insulation based on observations over the recovery voltage and classification of a tested insulation wear degree as well as to determine estimates of operating time until the insulation restoration or replacement with a new one. To classify the insulation quality conditions and to forecast its residual life until the restoration, a cluster analysis and discriminant analysis are used based on indicators reflecting the properties of an insulation recovery voltage curve. The study defines an information content received from frame insulation recovery voltage readings as compared with its resistance from the perspective of testing the insulation condition and predicting its residual life. Statistical characteristics of clusters corresponding to the insulation condition stages allow determining the limit values of predicting parameters and estimating the residual insulation life prior the restoration. To classify the insulation condition, an integrated evaluation of the insulation condition quality is proposed. Integrated evaluation is performed according to the recovery voltage curve and is used as an indicator during clustering. An experimental study of the proposed method practically confirmed its effectiveness.
Highlights
The analysis of “Ukrzaliznytsia” (Ukrainian Railways) locomotive fleet condition shows that the amount of damage happened to the TM winding insulation per one mln km of run has recently averaged from three to five cases
The prerequisite for selecting locomotives to be statistically relevant was the fact that locomotives underwent complete overhaul (CR) and TMs were fully restored
In accordance with the results obtained during the cluster breakdown procedure, we introduce the following stages of the frame insulation condition: 1st stage
Summary
The analysis of “Ukrzaliznytsia” (Ukrainian Railways) locomotive fleet condition shows that the amount of damage happened to the TM winding insulation per one mln km of run has recently averaged from three to five cases. Repair and in-between diagnostics in the locomotive economy of Ukrainian railways are periodic and subject to regulation. To perform the majority of measurements the equipment should be withdrawn from the operation. Such diagnostic system does not meet modern requirements. Continuous monitoring of the TM insulation technical condition becomes an optimal solution enabling reorganizing of the operational diagnostics. The introduction of automated monitoring will allow scheduling of equipment repairs and, if necessary, urgent TM withdrawing from operation. This approach will allow transferring from the outdated system of periodic maintenance to the maintenance matching the real technical condition of an object. Continuous monitoring systems need specific procedures, algorithms and models necessary for their effective functioning
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