STATE REGULATION OF NAMING AND SOFTWARE IDENTIFICATION IN VULNERABILITY MANAGEMENT PROCESSES

Electrical equipment (EE) is a key part of industrial electrical systems where unexpected mechanicalfailures in operation can cause serious consequences (disruption of the technological process, reductionin the quality and quantity of manufactured products and emergencies). For timely detection of suchfaults, as well as to ensure normal operation of the systems, it is required to conduct regular assessment ofEE technical state using modern computer technologies under conditions of incomplete and fuzzy information.To solve this problem, we propose an approach using quantization and convolutional neural networks(CNNs) which differs from existing approaches by complex processing of thermograms obtainedwith a thermal imaging device; images with black-and-white and color graphs obtained from instrumentsor built based on statistical data. This approach provides an opportunity to improve the accuracy of classificationof various EE malfunctions, reduce unscheduled equipment failures due to prompt decisionmakingregarding the EE technical state under conditions of incomplete and fuzzy information. The reviewof studies in this subject area by both Russian and foreign scientists reflects a number of successful experimentson the use of CNNs. The CNN developed to classify faults outputs a class number to which the currentstate of the equipment relates (class 1 – serviceable EE; class 2 – serviceable EE with small deviations).This paper considers a generalized scheme and algorithm of a complex approach to EE fault detectionwith their detailed description. The study results were obtained when diagnosing the asynchronousmotor АИР63А4У1 and confirm the validity and objectivity of using the proposed approach