International Scientific Journals
of Scientific Technical Union of Mechanical Engineering "Industry 4.0"

Existing approaches to the electric cyclic action bucket excavators’ technological operations identification are considered. The authors proposed the term “technological condition” of a single-bucket excavator as a combination of the current technological operation and data on the technical condition of the main components of the excavator. Based on the data analysed, a new method has been developed and proposed for identifying the technological state of excavators based on the mathematical apparatus and tools of neural networks and pattern recognition, which can be used in further studies. According to the methodology proposed by the authors using pattern recognition technologies based on the OpenCV library and a stereo pair formed from two Xiaomi cameras, experimental tests were carried out on a real object – an electric single-bucket excavator type “direct shovel” ECG-8I under various environmental conditions, including poor visibility. According to the test results, the proposed method showed high accuracy in technological operations identification – the identification error in the test sample did not exceed 5%, which indicates the adequacy of the constructed model. The scientific novelty of the work lies in the application of a method not previously applied in this field of technology, as well as in the proposed mathematical and simulation models. The practical novelty lies in the possibility of introducing this approach for the construction of automated management, moni toring and control systems, for solving the problems of weighing rock in an excavator bucket, determining the granulometric composition of rock in a bucket, as well as other identification problems.

This article is devoted to the problem of a passport for drilling and blasting operations formation, taking into account the main
characteristics. At most mining enterprises, this process is a manual calculation that leads to errors due to human factor and increases the
time it takes to generate drilling and blasting passport, and, as a consequence, the time for drilling and blasting.
The proposed solution is an automated complex that bases its calculations on the data of the cross-section mines shape, the dimensions of
the height and width of the mine and the cross-sectional area in the tunnel, the fortress on the scale of prof. M.M. Protodyakonov and the
thickness of the host rocks. All geometrical parameters of tunnel face are obtained automatically based on laser scanning. For further
calculations, intelligent algorithms are used, implemented using deep learning neural networks (with python tensorflow library). It is worth
noting that the final decision on the acceptance of the drilling and blasting passport is made by the person in charge. The result of using the
proposed system is automatically generated passport of drilling and blasting operations, including its alternative variations (due to the
passport chosen by the person in charge, the system will receive feedback to further improvement of the system algorithm).