Development of a SCADA-based real-tıme advanced monıtorıng system for the technıcal condıtıon of the tractıon motor
DOI:
https://doi.org/10.61089/aot2026.ydgd6v85Keywords:
SCADA systems, traction motors, multi-parameter diagnostics, real-time control, fault detectionAbstract
Reliable and continuous operation of electric locomotives requires continuous and accurate assessment of the technical condition of traction motors. Traditional SCADA monitoring systems used in rail transport are typically limited to independent threshold monitoring of a small number of electrical and thermal parameters, which reduces their sensitivity to complex and early-stage faults. This paper proposes a multiparameter approach to real-time traction motor technical condition diagnostics based on a formalized condition assessment model. The proposed methodology combines seven diagnostic parameters of electrical, thermal, mechanical, acoustic, and magnetic nature into a single diagnostic framework. Each parameter is converted into a normalized local condition index based on engineering threshold values, and its diagnostic significance is determined using correlation weighting. This is the basis for the formation of an integrated technical condition index, which is used to automatically classify motor condition into three classes: normal, warning, and critical. This article presents the Pearson correlation coefficients between diagnostic parameters and the degradation indicator, as well as the corresponding normalized weighting factors used to calculate the integral index. To quantitatively assess the diagnostic effectiveness, formal statistical validation was performed using a confusion matrix, as well as precision, recall, F1-score, and overall accuracy metrics. The approach was validated on a mixed dataset, including limited real-world operational measurements and simulated degradation scenarios in the Siemens TIA Portal environment, which allowed for verification of the diagnostic logic under controlled conditions. The obtained results demonstrate that multiparameter correlation-weighted diagnostics provide a more sensitive and interpretable assessment of the technical condition of a traction motor compared to traditional single-parameter SCADA monitoring and create a basis for the development of predictive maintenance for traction rolling stock.
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