Bootstrap method for monitoring the safety of traffic control systems of highly automated mining machines

The paper proposes an integrated approach of monitoring the safety of control systems of highly automated mining machines, including the structural model for the formation of this approach, which was used as a basis for specifying the vehicle safety criteria. A modern method of statistical analysis, i.e. the bootstrap method, was applied to analyze limited and nonrepresentative data samples on monitoring the safety of technical control systems of mining machines. The bootstrap method allows to efficiently work with limited and non-representative samples, which is especially relevant in the conditions of complex operational scenarios. Integration of this approach into monitoring and data analysis processes can significantly improve risk prediction, timely fault detection, and making management decisions when using highly automated mining machines. An algorithm for monitoring the safety of traffic control systems of highly automated mining machines is proposed based on the bootstrap method. This algorithm makes it possible to study the statistics of probability distributions for the control system parameters of highly automated mining machines based on multiple sample generation.