A smart system for monitoring the quality of mining complex software using real-time fault tolerance metrics

DOI: https://doi.org/10.30686/1609-9192-2025-5-105-112

Читать на русскоя языкеI.V. Samarin
National University of Oil and Gas (National Research University) “Gubkin University”, Moscow, Russian Federation
Russian Mining Industry №5 / 2025 p. 105-112

Abstract: Contemporary automated coal mining complexes are characterised with high level of integration of software and hardware, which makes the software quality assurance critically important for the safety and efficiency of the production processes. Traditional approaches to the software quality control in the mining industry demonstrate limited efficiency when dealing with dynamically changing operating conditions and the growing complexity of the software control systems for mining equipment. The aim of the study is to develop a smart software quality monitoring system that would integrate adaptive fault tolerance metrics to ensure continuous operation control of automated coal mining complexes. The study is based on a comprehensive methodology that includes telemetry data analysis, machine learning for failure prediction, and algorithms for dynamic assessment of the software quality. The empirical basis of the study covers software performance data from Russia's five largest coal mining companies for the period of 2021–2023, including more than 2.4 million records of faults, incidents, and performance metrics. Statistical analysis methods, deep learning neural networks, and clustering algorithms were used to identify the patterns of software quality degradation. The results of the study demonstrate that the proposed system reduces the detection time of critical faults by 67.3% as compared to the traditional methods. The accuracy coefficient of the failure prediction was 0.891, which exceeds the level of the existing solutions by 23.4%. The developed metrics of flaw tolerance showed a correlation with the actual incidents at the level of 0.847, confirming their predictive value. The practical significance of the research lies in the possibility of applying the developed system to improve the reliability of mining equipment and to reduce the operational risks. The theoretical value is defined by the contribution to the development of the methodology for assessing the software quality for critical industrial systems.

Keywords: software, mining complexes, fault tolerance, metrics of fault tolerance, real-time monitoring, machine learning, automated control systems, coal mining

For citation: Samarin I.V. A smart system for monitoring the quality of mining complex software using real-time fault tolerance metrics. Russian Mining Industry. 2025;(5):105–112. (In Russ.) https://doi.org/10.30686/1609-9192-2025-5-105-112

Article info

Received: 20.06.2025

Revised: 01.08.2025

Accepted: 04.08.2025

Information about the author

Ilya V. Samarin – Dr. Sci. (Eng.), Professor, Head of the Department of Automation of Technological Processes, National University of Oil and Gas (National Research University) “Gubkin University”, Moscow, Russian Federation; https://orcid.org/0000-0003-2430-5311 ; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

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