Classification of Dynamic Operating Modes of Electric Motors Using Synthetic Load Profiles

The analysis of transient regimes is important for the reliable monitoring and diagnostics of electromechanical systems, since dynamic changes in load have a significant impact on the electrical and mechanical characteristics of electric motors. In this study, an approach for classification of dynamic operating regimes is proposed using ten synthetically generated load profiles representing linear, nonlinear, periodic, stochastic, and combined loads. An experimental setup was developed for implementing controlled load effects and recording electrical and mechanical parameters of DC and induction electric motors. To determine the most informative characteristics, the ReliefF, SFCPP, and FSNCA methods were applied, followed by dimensionality reduction and classification using statistical and machine-learning approaches. The selected feature vectors were evaluated by stratified 5-fold cross-validation and validated using Wilcoxon, Friedman, and permutation tests. The results obtained show that a limited set of electrical and electromechanical parameters contains sufficient information to reliably distinguish between different dynamic operating modes. The best-performing model achieved classification accuracy above 98%, confirming the effectiveness of the proposed framework. The proposed approach provides a reproducible methodology for generating representative datasets, assessing the informativeness of the features, and supporting intelligent systems for monitoring the condition of electric drives. The methodology is applicable both in laboratory conditions and in the development of digital twins and predictive maintenance systems.