Probabilistic analysis of pump reliability indicators using a neural network

In the conditions of mechanization and automation, the urgency of the problem of increasing the reliability of the equipment is increasing. Evaluation of indicators of the risk of operation of pumps is carried out using a model called «event tree». Because of the limited information, the results of probabilistic security analysis are characterized by a high degree of uncertainty. To collect information about the condition of mining machines and equipment, onboard logs, electronic tables and specialized software such as the SAP Plant Maintenance (SAP PM) system are used. Collection, processing and analysis of statistical information about failures allows to establish patterns of failure of mining machines and equipment, working units and aggregates, etc. Based on the algorithm of probabilistic analysis of the safety of pumps with the use of artificial intelligence. It is shown that the prediction of error-free operation with the help of a neural network allows to specify the technical condition of pumps. Determined reliability indicators of the large synchronous motor of the pumping unit of the pumping station, taking into account the possible excess temperature of the winding in the overload mode. In the work, it was revealed that when the temperature of the motor windings is exceeded, the probability of error-free operation decreases by 10%. Optimized design of tools for identifying failures of elements of large pumps in the mining industry taking into account operating modes.

Tynchenko Ya. A., Kukartsev V. V., Bashmur K. A., Сяоган Ву, Sevryugin N. S. Probabilistic analysis of pump reliability indicators using a neural network. MIAB. Mining Inf. Anal. Bull. 2024;(7-1):126-136. [In Russ]. DOI: 10.25018/0236_1493_2024_71_0_126.

Yadviga A. Tynchenko¹, Bauman Moscow State Technical University, 105005, Moscow, Russia, e-mail: t080801@yandex.ru,
V.V. Kukartsev^1,2, Cand. Sci. (Eng.), Assistant Professor, Artificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005, Moscow, Russia, e-mail: vlad_saa_2000@mail.ru, Researcher ID: U-6956-2019, Scopus Author ID: 57202283852, SPIN-code: 3522-2910, ORCID ID: 0000-0001-6382-1736,
K.A. Bashmur¹, Senior Lecturer, 
Xiaogang Wu, School of Electrical Engineering, Hebei University of Technology, Tianjin 300401, China, e-mail: xgwu@hrbust.edu.cn, ORCID ID: 0000-0002-1830-0437,
N.S. Sevryugina, Dr. Sci. (Eng.), Professor,  V.P. Goryachkin Institute of Mechanics and Power Engineering, Russian State Agrarian University — Moscow Timiryazev Agricultural Academy, 127550, Moscow, Russia, e-mail: nssevr@yandex.ru, Researcher ID: M-2198-2015, Scopus Author ID: 56426993600, SPIN-code: 4444-0443, ORCID ID: 0000-0002-3494-1437.
¹ Siberian Federal University, 660041, Krasnoyarsk, Russia,
² Reshetnev Siberian State University of Science and Technology, 660037, Krasnoyarsk, Russia.

V.V. Kukartsev, e-mail: vlad_saa_2000@mail.ru.

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