Abstract
The article presents a new method for diagnosing the technical condition of radio-electronic components, combining the methods of thermal diagnostics with the technologies of artificial neural networks. The structure of the method is shown, and the composition of the functional blocks is determined. The implementation of the method is a symbiosis of technologies for mathematical and simulation modeling of the technical state of a radio-electronic device with its physical tests and research of characteristics. When developing the method, specialized software tools for design and circuit design were actively used, such as Altium Designer CAD, SolidWorks, NI Multisim, the FloTHERM PCB thermal analysis module, as well as the MATLAB mathematical modeling and calculation package. With the help of these tools, a number of studies were carried out, including sets of numerical values of the power of circuit elements and temperature indicators of the printing unit, both for the correct state of the device and in states with artificially introduced defects. They, in turn, became the basis of the database of electronic node failures. To implement diagnostic procedures and identify the technical condition, an artificial neural network based on selforganizing Kohonen maps was created, its structure, parameters and algorithms of functioning were determined. The diagnostic procedure is based on the analysis of information from the fault database and its comparison with experimental data obtained as a result of a physical experiment. The results of the study showed that the network automatically classifies the characteristic defects of electronic components using the algorithms embedded in it. The list of characteristic defects in the proposed diagnostic method is limited to a discrete set of the most common faults, because, as their number increases, the use of the self-organizing Kohonen network for automatic classification becomes much more complicated and ineffective in terms of performance and reliability of identification. Among the advantages of this technology, it should be noted that the Kohonen network has the ability to convert largedimensional input data into a two-dimensional array. So, the results are easy to visualize and convenient to use when generating reports and recommendations for subsequent decision-making about the possibility of using an electronic device.
Highlights
Financial disclosure: Authors have no a financial or property interest in any material or method mentioned
The article presents a new method for diagnosing the technical condition of radio-electronic components, combining the methods of thermal diagnostics with the technologies of artificial neural networks
The implementation of the method is a symbiosis of technologies for mathematical and simulation modeling of the technical state of a radio-electronic device with its physical tests and research of characteristics
Summary
Представлен оригинальный метод диагностирования технического состояния печатных узлов (ПУ) радиоэлектронных средств (РЭС), основанный на математическом моделировании тепловых процессов и сети Кохонена как инструменте классификации конструктивных дефектов ПУ. Для реализации диагностических процедур и идентификации технического состояния создана искусственная нейронная сеть на основе самоорганизующихся карт Кохонена, определена ее структура, параметры и алгоритмы функционирования. Перечень характерных дефектов в предложенном методе диагностирования ограничен дискретным набором наиболее часто встречающихся неисправностей, поскольку при увеличении их числа применение самоорганизующейся сети Кохонена для классификации значительно усложняется и становится неэффективным по показателям производительности и достоверности идентификации. Из достоинств данной технологии следует отметить, что сеть Кохонена имеет возможность преобразовывать входные данные большой размерности в двумерный массив, поэтому результаты легко визуализировать и удобно использовать при формировании отчетов и рекомендаций для последующего принятия решения о возможности эксплуатации электронного устройства. Алгоритм Кохонена в задачах классификации конструктивных дефектов печатных узлов.
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