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.

The results of tests for resistance to abrasive wear on highly abrasive hard rock white electrocorundum are presented. The main material of fast-wearing elements of mining and processing equipment-110G13L steel (Gadfield steel) in comparison with other 9 grades of steel and cast iron, including specially developed wear-resistant foreign steels such as Hardox and Miiluks, is analyzed. The studies were carried out using an experimental stand for studying the material wearing process. On the stand the sample was fixed in a holding device and, after being brought into contact with the abrasive, it was rotated under a constant load. As a result of the experiments, it was confirmed that the order of placement of the tested materials in terms of increasing wear resistance coincides with their placement in terms of increasing hardness. At the same time, the wear resistance of the most resistant material – U8A steel after quenching – is about 4 times higher than this indicator for the least resistant components – low-carbon steel 25L, including gray and high-strength cast iron SCH21, VCH35. The wear resistance of 110G13L steel, as well as 65G, U8 steels in the hardened state, is from 1.5 to 2 times higher than that of foreign steels M400, H450, M500, H500. The results of the conducted studies allow us to evaluate the analyzed materials on the basis of their wear resistance and hardness indicators on the feasibility of using them in the manufacture of fast-wearing parts of mining equipment. Based on the research data, it seems promising to develop new ways to increase the wear resistance of domestic steel, including 110G13L steel traditionally used in mining.

The research considers conducting orthogonal experiment (OT) as one of the stages in developing a new discrete element method (DEM) parameters calibration approach. The measured responses in experiment are the parameters obtained by DEM animation processing using machine vision system (MVS). The variable factors in experiment are DEM parameters. A brief overview of an existing calibration approaches given in the article. The choice of OT as a design of experiment tool among other mathematical tools discussed. Experiments conducted using specially developed rig where bulk material's flow captured as DEM animation. DEM animation converted to video and then processed using MVS that allow register the values of such parameters as angle of repose or expiration time (measured responses). The results of the OT show that it is possible to identify four measured responses with the most valuable correlation coefficient. DEM parameters with the biggest influence on the measured responses identified for each of the obtained regression. Obtained results are useful in learning or iterative algorithms development for DEM parameters calibration.