Risk assessment of horizontal transport system in a copper mine

execution of production system goals. Thus, manufacturing
companies need to evaluate and react to the risk as well as it is
possible. Due to the complexity and variable character of the mining
system, as well as different types of parameters obtained at
subsequent stages of the horizontal transport process, the risk was
assessed according to various methods. Three stages were
distinguished in the horizontal transport system of copper ore studied
in the paper: tyre haulage of the shot copper ore to the transfer point,
the so-called grate, transferring the output to belt conveyors and
crushing solid rock into smaller pieces; transport on belt conveyors.
Risk factors have been characterized for each stage. To assess the risk
of the loading and haulage processes, an ANN model was built to
predict the amount of ore extracted. A general linear neural network
model was also built to confirm the results of the correlation analysis.
One of the methods recommended for risk assessment in
manufacturing companies is the Failure Mode and Effects Analysis
(FMEA), which allows for calculating the risk and prioritizing it. The
FMEA method allows identifying only these elements of the
production system that are the most sensitive to the impact of risk
factors. For the next stages of the horizontal transport process, the
risk was assessed according to the FMEA method. The risk levels at
each stage were different. Production systems are composed of many
elements that create the reliability structure of this system.
Unfortunately, the FMEA method does not allow for analyzing the
reliability structure of the production system. Therefore, the new idea
presented in this paper is a method of total risk assessment of the
horizontal transport system of copper ore, which does take its
reliability structure into account. The currently used methods of risk
analysis and assessment do not take into consideration the reliability
structure of the production system. The proposed method can be
applied for risk assessment in other production systems characterized
by a diversified structure.