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The reliability and longevity of electrical machines, such as motors and generators, are critically determined by the condition of their insulation system. This paper presents a comprehensive analysis of the multifaceted processes leading to the degradation of winding insulation materials. The study reveals the complex and often non-linear interrelationships between key aging factors thermal overloading, electrical surges, mechanical vibrations, and the detrimental impact of the environment (humidity, contaminants). The combined influence of these factors leads to a progressive deterioration of dielectric strength and, consequently, a reduction in the equipment's service life. As a methodological foundation, an innovative approach to insulation condition diagnostics, based on graph modeling, is proposed. The developed graph model serves as a formalized tool for describing cause-and-effect relationships between input parameters (armature current, voltage, and start/stop regimes), external operating conditions, and internal diagnostic parameters, such as insulation resistance, tangent delta, and partial discharge characteristics. Particular attention in the model is paid to the analysis of positive feedback loops, which explain the non-linear, avalanche-like nature of damage development, where one type of defect accelerates the progression of others. The practical significance of the research lies in the transition from traditional planned-preventive maintenance to a predictive model. The proposed graph model enables the early diagnosis of degradation signs and the construction of accurate forecasts for the insulation's remaining useful life. The results of the work pave the way for the development of intelligent monitoring and diagnostic systems, as well as for the optimization of maintenance strategies for power electrical equipment, ultimately enhancing its operational reliability and economic efficiency. The developed graph model will serve as a theoretical basis for creating effective diagnostic systems and predicting the remaining useful life of insulation. Identifying positive feedback loops in degradation processes makes it possible to determine critical control points and develop preventive measures to prevent sudden failures.

The issue of designing devices with optimal characteristics of converting an electrical signal and its shape without distortion is relevant when developing a device with small dimensions for electric rolling stock. In this article, the influence of transformer parameters on the form of voltage transmission is considered and recommendations are given on the choice of values of these parameters. This applies to pulse, differential, and low-power transformers. The study of this issue is moving from theory to constructive calculation. In the era of digital twin design, the computational mathematical model of the transformer will allow for a quick decision on compliance with the inherent requirements for the product. Being universal, the proposed model allows performing calculations for any pulse transformer, provided that the appropriate initial data is specified and will allow obtaining an estimated characteristic of the transformer already at the design stage. The study showed the relationship between the transformer design, the materials used for the magnetic circuit and the shape of the output signal. Obviously, the dissipation inductance and the capacitance of the windings are the main parameters that introduce distortion into the signal. Modeling of the winding design and the selection of magnetic alloys or ferrites help to reduce eddy currents in the magnetic circuit, which improves the characteristics of the output signal and reduces the effect of the constant component of the current at the end of the pulse. A transformer model with reduced core dimensions made of steel with a smaller rolled thickness and a homogeneous domain structure, as well as the use of a distributed winding along the core, which has a minimum intrinsic capacitance, had the least effect on the output signal with an acceptable signal distortion of 3 %. The resulting transformer model can be used to create a finished product.