With the rapid development of digital technologies such as IoT, AI, and big data, electrical energy consumption is rapidly increasing. Electrical facilities that supply electrical energy are operated with high reliability and stability for end-of-life time. In addition, depending on the type of electrical load that consumes electrical energy in various forms, electrical insulation systems deteriorate due to electrical and thermal stress, which reduces electrical and mechanical insulation strength. Due to such continuous stress and electrical transient phenomena, electrical facilities may experience electrical accidents due to electrical insulation breakdown before the expected design lifetime. In addition, periodic inspections according to related regulations must be conducted to prevent unexpected electrical accidents, but this leads to problems in which the electrical facilities cannot be turned off. Therefore, it is believed that an uninterruptible diagnostic judgment technique that determines compliance with related regulations such as electrical facility technology standards, internal wiring regulations, and inspection regulations without turning off the electrical facilities and at the same time detects abnormal conditions of the facilities early, it is possible to prevent electrical accidents and improve the efficiency of electrical facilities. In this paper, we propose an uninterruptible power diagnosis judgment technique that can prevent or reduce electrical accidents in cast-iron transformers by applying judgment criteria of diagnostic sensors for various types of measurement parameters that can diagnose and evaluate the presence or absence of abnormalities in electrical equipment, including partial discharge, and AI algorithms learned from data of diagnostic sensors.