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The purpose of this paper is to analyse and compare the downtime of four drilling machines used in two underground mines in Sweden. The downtime of these machines was compared to show what problems affect downtime and which strategies should be applied to reduce it.

The study collects failure data from a two-year period for four drilling machines and performs reliability analysis. It also performs downtime analysis utilising a log-log diagram with a confidence interval.

There are notable differences in the downtime of most of the studied components for all machines. The hoses and feeder have relatively high downtime. Depending on their downtime, the significant components can be ranked in three groups. The downtime of the studied components is due to reliability problems. The study suggests the need to improve the reliability of critical components to reduce the downtime of drilling machines.

The method of analysing the downtime, identifying dominant factors and the interval estimation for the downtime, has never been studied on drilling machines. The research proposed in this paper provides a general method to link downtime analysis with potential component improvement. To increase the statistical accuracy; four case studies was performed in two different mines with completely different working environment and ore properties. Using the above method showed which components need to be improved and suggestions for improvement was proposed and will be implemented accordingly.

The purpose of this paper is to develop a practical economic replacement decision model to identify the economic lifetime of the ventilation system used by Trafikverket in its Stockholm tunnels.

The proposed data-driven optimisation model considers operating and maintenance costs, purchase price and system resale value for a ventilation system consisting of 121 fans. The study identified data quality problems in Trafikverket’s MAXIMO database.

It is found that the absolute economic replacement time (ERT) of the ventilation system is 108 months but for a range of 100–120 months, the total cost remains almost constant. Sensitivity and regression analysis showed that the operating cost has the largest impact on the ERT.

The results are promising; the company has the possibility of significantly reducing the LCC of the ventilation system by optimising its lifetime. In addition, the proposed model can be used for other systems with repairable components, making it applicable, useful and implementable within Trafikverket more generally.

The purpose of this paper is to present a practical model to determine the economic replacement time (ERT) of production machines. The objective is to minimise the total cost of capital equipment, where total cost includes acquisition, operating, maintenance costs and costs related to the machine’s downtime. The costs related to the machine’s downtime are represented by the costs of using a redundant machine.

In total, four years of cost data are collected. Data are analysed, practical optimisation model is developed and regression analysis is done to estimate the drilling rigs ERT. The artificial neural network (ANN) technique is used to identify the effect of factors influencing the ERT of the drilling rigs.

The results show that the redundant rig cost has the largest impact on ERT, followed by acquisition, maintenance and operating costs. The study also finds that increasing redundant costs per hour have a negative effect on ERT, while decreases in other costs have a positive effect. Regression analysis shows a linear relationship between the cost factors and ERT.

The proposed approach can be used by the decision maker in determining the ERT of production machines which used in mining industry.

The research proposed in this paper provides and develops an optimisation model for ERT of mining machines. This research also identifies and explains the factors that have the largest impact on the production machine’s ERT. This model for estimating the ERT has never been studied on mining drilling rigs.