Electromechanical systems operating under viscoelastic loads require precise modeling due to the highly nonlinear behavior of the load. An automatic bread machine is a practical example where dough represents a dynamic viscoelastic load sensitive to hydration. As found in this paper, increasing the water content leads to a decrease in the torque and the required mechanical power. An integrated approach combining MATLAB/Simulink and Simscape modeling, experimental measurements, and a PCA-based regression model is presented. The tests were conducted with three types of flour (type 500, type 1850, and rye–wheat) at hydrations of 52%, 58%, and 63% with over 6000 measurements recorded for each combination. The regression models achieve moderate predictability (R² = 0.64–0.96) model performance that varies across flour types. Increasing the dough hydration from 52% to 63% reduces the torque by approximately 22–46% across the tested flour types, while the angular velocity rises slightly (from about 147.9 to 151.9 rad/s). A descriptive decrease in energy consumption of up to around 6% was observed within the sampled batches with the system efficiency remaining within a narrow range around η ≈ 0.67. Within the studied levels (52–63%), the minimum load was observed at 58%. The proposed integrated model reliably describes the interaction between the electric motor, the mechanical gear, and the viscoelastic load, and it offers a basis for energy optimization and the implementation of low-cost sensor systems for intelligent control in the bread-making process.

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.