Russian Federation
Russian Federation
Russian Federation
Russian Federation
UDC 629
The objective of the study is to develop and validate a methodology for quickly assessing the technical condition of a traction motor and critical components, which forms the foundation for the transition from traditional scheduled preventive maintenance to modern predictive maintenance. This enables a shift from scheduled intervals to condition-based maintenance. As part of the study, a comprehensive MATLABbased simulation model was created for the EP21-DTA-1200A electric locomotive motor used in pushpull trains. The model implements key diagnostic methods: vibration analysis to identify mechanical defects (specifically, SKF series bearings), thermal monitoring to prevent overheating, and stator current harmonic analysis for the early detection of electrical faults. To demonstrate the system’s operation, the model includes a scenario simulating a bearing outer ring defect, characterized by an increase in vibration amplitude at the characteristic BPFO frequency. The main result is the proposed architecture of an integrated diagnostic system, including data collection, signal processing, analytics, and decisionmaking layers. An algorithm based on weighted indices generates specific recommendations, ranging from continued operation to immediate shutdown. The practical significance of this work lies in the fact that implementing this methodology on the Russian railway network will improve the reliability and safety of high-speed rolling stock, reduce the cost of unscheduled repairs, and optimize maintenance logistics by moving it to a predictive basis.
diagnostics, high-speed train, push-pull, propulsion system, engine components, monitoring
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