В статье приведены результаты исследований применения фреймворка автоматического создания алгоритмов машинного обучения (AutoML) для решения задач диагностики и оценки технического состояния электродвигателей, применяемых в широком спектре в горнодобывающей промышленности. Представлена работа и функционирование AutoML Фреймворка – FedotIndustrial, разработанного автором. Предложен в качестве экспериментального и практического применения испытательный стенд, на котором при помощи электродвигателей переменного тока формируются условия изменения нагрузки и величины дефекта в виде несоосности. Выявлено, что более информативной является интерпретация данных в виде годографа Парка и диагностической кривой. Установлено что, при решении задача классификации данных с учителем, формируются разметка данных на классы, образующих в совокупности 6 классов технического состояния электродвигателя. Результаты исследований могут быть полезными при дальнейшей разработке предиктивных систем автоматизации и защиты электромеханического оборудования горнодобывающих предприятий.

Today the industry is facing a shortage of skilled workers and an aging workforce, which will eventually lead to a loss of knowledge. But augmented reality technology can connect field workers with experts who are able to provide remote guidance in real time. The subsequent advantage is that the information obtained by AR devices can be used as accumulated successful experience in the future, which facilitates decision-making in specific business processes of the company. With AR, employees gain experience and skills much faster. This paper shows the application of augmented reality technology in the maintenance of electromechanical equipment. The main functions of the augmented reality system for servicing electrical equipment are presented, the solution to the problem of integrating an augmented reality software application with existing automation systems is shown, and the methods of interaction of the developed software module with third-party modules, for example, various analytical modules, etc. are described.

This paper presents a numerical simulation of powder sintering. The numerical model presented in this paper uses the discrete element method, which suggests that the material can be modeled by a large set of discrete elements (particles) of a spherical shape that interact with each other. A methodology has been developed to determine the DEM parameters of bulk materials based on machine vision and a neural network algorithm. The approach is suitable for obtaining the exact values of the DEM parameters of the investigated bulk material by comparing the visual images of the material’s behavior at the experimental stand in reality and in the model. Simulation of sintering requires an introduction of cohesive interaction between particles representing interparticle sintering forces. Numerical sintering studies were supplemented with experimental studies that provided data for calibration and model validation. The experimental results have shown a significant capability of the designed numerical model in modeling sintering processes. Evolution of microstructure and density during sintering have been studied under the laboratory conditions.