FORMALIZATION AND SYNTHESIS OF MEMBERSHIP FUNCTIONS FOR QUANTITATIVE DIAGNOSTIC PARAMETERS: TEMPERATURE AS A FUNDAMENTAL INDICATOR OF POWER EQUIPMENT CONDITION

This paper examines the formalization and synthesis of fuzzy membership functions for temperature as a fundamental quantitative diagnostic parameter of power equipment. Temperature-based condition assessment is considered for electrical transformers and other high-voltage equipment, with emphasis on insulation aging, thermal degradation, and predictive maintenance. The proposed methodology includes data acquisition and preprocessing, statistical characterization, initial membership function generation, optimization and refinement, and validation. Fuzzy membership functions transform quantitative temperature measurements into interpretable linguistic condition states and can be integrated with other diagnostic parameters to improve early fault detection and equipment reliability.

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