This article presents a part of the industrial metaverse for electric drive system diagnostics. The advantages of using a low-code/no-code platform for electric drive systems diagnostics are demonstrated. Five diagnostic scenarios were developed, programmed, and implemented. The article demonstrates the implementation and use of the platform’s main functional blocks: a visualization block (which displays the state of electric machines in any user-friendly form—graphs, Park’s vector diagrams, or diagnostic curves); a digital twin block (which simulates various engine states); a digital twin block with an engine defect (which simulates faulty engine states); and an artificial intelligence block (which trains classification model to predict various engine states). Experiments on training the artificial intelligence block using a misalignment defect dataset are presented. The dataset was divided into six classes: engine operation with/without a defect under no load, engine operation with/without a defect under a 50% load, and engine operation with/without a defect under a 100% load. The workflow for training and using the model, the basic training approaches, and the distinguishability of the presented classes are demonstrated. The model training results are shown. The article presents a methodology for extensive testing of program functionality. The obtained results demonstrate the feasibility of implementing a low-code/no-code platform and the feasibility of solving the assigned tasks with its help, as well as the simplification and reduction in engineering solution development time.

Grinding is the most energy-intensive process among all stages of raw material preparation and determines the course of subsequent ore beneficiation stages. Level of electricity consumption is determined in accordance with load characteristics forming as a result of ore destruction in the mill. Mill drum speed is one of process variables due to which it is possible to control ore destruction mechanisms when choosing speed operation mode of adjustable electric mill drive. This study on increasing energy efficiency due to using mill electric drive is based on integrated modelling of process equipment – grinding process and electromechanic equipment – electric drive of grinding process. Evaluating load torque by means of its decomposition into a spectrum, mill condition is identified by changing signs of frequency components of torque spectrum; and when studying electromagnetic torque of electric drive, grinding process is monitored. Evaluation and selection of efficient operation mode of electric drive is based on the obtained spectrum of electromagnetic torque. Research results showed that with increasing mill drum speed – increasing impact energy, load torque values are comparable for the assigned simulation parameters. From the spectra obtained, it is possible to identify mill load condition – speed and fill level. This approach allows evaluating the impact of changes in process variables of grinding process on parameters of electromechanical system. Changing speed operation mode will increase grinding productivity by reducing the time of ore grinding and will not lead to growth of energy consumption. Integration of digital models of the technological process and automated electric drive system allows forming the basis for developing integrated methods of monitoring and evaluation of energy efficiency of the entire technological chain of ore beneficiation.

Представлены результаты экспериментов по влиянию энергии удара на интенсивность ударно-абразивного изнашивания модельного инструмента из трех марок инструментальных сталей вида конической пики при ударном взаимодействии с фрагментом гранита. Часть инструмента подвергалась типовой термической обработке, практикуемой на заводах-изготовителях пик, другая — дополнительной обработке холодом или криогенной обработке. Для обоснования возможности переноса экспериментальных данных на натурные условия устанавливались рабочие параметры эксперимента, при соблюдении которых его условия будут подобны условиям работы пики гидромолота. Для выполнения указанного подобия уровень контактных напряжений между модельным инструментом и породой в экспериментах был таким же, как и при ударном внедрении пики. Установлена зависимость между интенсивностью ударно-абразивного изнашивания и энергией удара модельного инструмента, на основании которой рассчитаны интенсивности изнашивания пики гидромолота JCB HM380 при разрушении гранитных фрагментов. Оценивалась наработка пики, за которую принималось количество нанесенных ей ударов до полного изнашивания острия. Из сравнения наработки пик, изготовленных из анализируемых сталей и подвергнутых указанным видам термической обработки, сделан вывод о целесообразности внедрения операций обработки холодом и криогенной обработки в технологический процесс изготовления пик гидромолотов.