Digital Twin for Integration of Control and Diagnostics of Electromechanical Systems Under Uncertainty

Abstract

The study aimed to create a digital twin for the integration of control and diagnostics of electromechanical systems under conditions of uncertainty, with minimal reliance on physical sensors. The research was conducted at Mykolaiv National Agrarian University. Physically based models were developed for thermal processes in windings, assessment of losses in magnetic conductors, and wear indicators for components, virtual sensors, signal filtering algorithms and degradation prediction were implemented, and verification was conducted on test benches and in computer modelling. Quantitative results were obtained, which constitute the main contribution of the work: the accuracy of reproducing hidden parameters was 93.6-97%, the relative error of reproducing losses in the transformer was 3%, the relative error of thermal estimates was 3.5-6.8%, the correlation with reference measurements reached 0.99; the reduction in the dispersion of noisy signals was 33-41%, the signal-to-noise ratio increased by 4.2-6.7 decibels, and the root mean square error decreased by 35-44% with an additional delay of no more than 0.04 seconds. The forecast of the time to failure of the hydraulic unit provided 92% correct estimates within a tolerance of ±10% for a 48-hour horizon; in the traction electric drive, a drop in efficiency (efficiency) by 6.7 percentage points under conditions of magnetic saturation was confirmed; in ship drives, peak torsional loads were reduced by 11%; in biogas plants, the energy balance error was 5.4%; in irrigation systems, energy consumption was reduced by 9%; in robotics, the accuracy of deviation detection was increased by 14%. The models can be used in ship drives, biogas plants, robotic lines, irrigation pumping stations, transformer substations, hydraulic drives and traction electric drives, reducing downtime and energy consumption without changing the existing control infrastructure.