Utilization of the shovel fleet as a capital-intensive and operationally important asset in open-pit mines is a key indicator for mine production analysis. This paper investigates shovel utilization in surface mining using a novel smart platform integrated with the shovel operating joystick. It utilizes a unique algorithm to identify and differentiate operational and non-operational time based on comparing real-time data and average loading cycle time. This data is then employed to calculate overall uptime and identify downtime periods. A field study was carried out on six electric cable shovels consisting of P&H 2100 and TZ WK-12, at Sarcheshmeh Copper Mine. The analysis revealed that the average utilization of the whole fleet is equal to 33%, ranging from 16 to 48%, which is dramatically lower than the mine expectations. The statistical analysis showed that in 10–13% of the operating time, the utilization is higher than 75%, which is a moderately acceptable level. Finally, according to the outcomes of the field study and the developed smart platform, it could be concluded that improvements in dispatching system accuracy, revising the grade blending strategies, increasing processing plant flexibility and improved operator training could enhance shovel fleet utilization and whole mine productivity.

Work-related injuries, particularly musculoskeletal disorders (MSDs), incur significant costs for companies in terms of sick leave and reduced productivity. Maintaining correct ergonomic posture is crucial to prevent these injuries and mitigate the impact of psychosocial factors. Digital technology plays a vital role in creating efficient and flexible work environments that cater to individual needs. Rather than relying solely on specialists, workers can utilize digital applications to prevent workload and strain injuries. This study investigates the effectiveness of a digital AI-based intervention program aimed at preventing work-related injuries and improving the physical work environment by addressing musculoskeletal disorders caused by incorrect postures. Through interviews with tool users in an industry setting, a web-based prototype application was tested to enhance workplace safety and improve physical health. The application employs digital AI tools to provide real-time feedback to workers. The interviews specifically assess how users evaluate and effectively utilize the tool to enhance working postures and the overall work environment. The study seeks to evaluate the efficacy of the digital AI-based intervention program and gather insights on users’ perceptions and utilization of the application. This research has the potential to contribute to a safer and healthier workplace by harnessing the power of technology. The study seeks to evaluate the efficacy of the digital AI-based intervention program and gather insights on users’ perceptions and utilization of the application.

Despite the high cost and low efficiency, compressed air is mostly used in underground mining for ore extraction, hoisting, and mineral processing operations. Failures of compressed air systems not only threaten the health and safety of workers but also contribute to inefficient control of airflow and stopped all equipment that operates by compressed air. In such uncertain conditions, mine managers are faced with the big challenge to supply enough compressed air, and therefore, the reliability evaluation of these systems is essential. This paper aims to analyze the reliability of the compressed air system using the Markov modeling approach as a case study, Qaleh-Zari Copper Mine, Iran. To achieve this, the state space diagram was constructed considering all relevant states for all compressors in the main compressor house of the mine. The failure and repair rate of all main and reserve compressors were calculated for all possible transitions between states to obtain the probability of being of the system in each of the states. Moreover, the probability of failure at any time period was considered to study the reliability behavior. The results of this study show that there is 31.5% probability that the compressed air supplying system is in operating condition with two main and one standby compressors. The system probability that two main compressors are remain in the operation without failure for one months is 92.32%. Furthermore, the lifetime of the system is estimated 33 months when at least one main compressor is active.

Reliability of mining equipment is influenced by different operational and environmental risk factors. However, in practice, including all relevant factors in reliability analysis is not possible. Ignoring the risk factors leads to unobserved heterogeneity and biased estimation of reliability characteristics. This research proposes a semi-parametric mixed-effects model to assess the simultaneous effects of observed and unobserved risk factors on reliability. Furthermore, the model can be used for investigating inter-cluster dependency and variability. To illustrate the model, a comprehensive case study is presented using data from three mines in Iran. The results show that the model can address the effect of unobserved risk factors. Moreover, it is found that the hazard of dump trucks is significantly associated with operator skill, season and the elevation difference between dumping and loading points.

The large tonnage mines use railway transportation as a system for mineral transportation. The rolling stock health condition study and improvement not only reduce the lifecycle cost of the assets but also ensures safe, reliable, punctual, and efficient transportation. The remaining useful life estimation is an efficient approach to condition-based maintenance, prognostics, and health management. This paper aims to predict the remaining life of a mining rolling stocks in the presence of operational environmental effects. In this study, the operation and failure data were obtained from Malmbana, LKAB, Sweden, a Swedish Railway Company, considering the effective operational factors. The failure behaviour of the railcar was evaluated and then the proportional hazard model was used to estimate the conditional reliability functions and accordingly the remaining useful life at the various initial survival time. Finally, the reliability-based time interval is applied to plan the maintenance operations. Results of this study show that the operator’s skill level and the maintenance quality had a significant influence on the reliability performance. By considering the proposed PM plan according to the desired reliability level, the remaining lifetime of the rolling stock will be improved by 78.10%, on average.

Reliability is widely used as a performance indicator of mining equipment to achieve a cost-effective maintenance plan. Reliability is a function of time as well as environmental and operational factors. Applying an adequate model by taking into account the mentioned factors is vital to ensure an accurate estimation of reliability characteristics. The aim of this study is to investigate the application of mixed frailty model to describe both observed and unobserved heterogeneity in reliability analysis of mining equipment. The capability of the model is assessed using field data from a fleet of dump trucks in an open-pit mine. The results indicate that the proposed model is superior to the traditional Cox model when data are heterogeneous. The results also show that the operator's skill and road conditions have a significant effect on the reliability of dump trucks.

Railway transportation systems are generally used to transport minerals from large-scale mines. Any failure in the railcar components may cause delays or even catastrophic derailment accidents. Failure mode and effect analysis (FMEA) is an effective tool for the risk assessment of mechanical systems. This method is an appropriate approach to identify the critical failure modes and provide proper control measures to reduce the level of risk. This research aims to propose an approach to identify and prioritize the failure modes based on their importance degree. To achieve this, the analytical hierarchy process (AHP) is used along with the FMEA. To compensate for the scarcities of the conventional FMEA in using the linguistic variables, the proposed approach is developed under the fuzzy environment. The proposed approach was applied in a case study, a rolling stock operated in an iron ore mine located in Sweden. The results of this study are helpful to identify not only the most important failure modes but also the most serious and critical ones.

Drilling, as the initial stage of exploitation in open-pit mining, has a significant effect on subsequent mining stages. Any failure in a drilling machine stops the drilling operation and, as a result, all following operational phases come to a halt. The drilling system is the most important part of the drilling operation. This study selected two new rotary-type drilling machines (A and B) in Sarcheshmeh Copper Mine in Iran as a case study to perform a reliability and availability analysis of their drilling systems. The drilling system was divided into three independent subsystems; drill string, rotary head, and mast. The reliability modeling used a reliability block diagram and Monte Carlo simulation. Reliability and availability were evaluated, and the importance of each subsystem estimated. A preventive maintenance schedule was proposed using weighed reliability allocation to achieve the reliability target. The results show the mast is the most reliable subsystem. Importance measure analysis indicates the drill string and rotary head are the most critical subsystems of machines A and B, respectively. Finally, under the proposed maintenance policy, the reliability of the drilling systems of machines A and B is improved.

Railcar or Rolling stock that is referring to all vehicles moving on railway, is one of the most important component of the rail transport system. In-operation failures of the railcar results delays in transportation and therefore predicting the remaining useful life (RUL) and accordingly considering the preventive maintenance activities are essential. The RUL estimation is a key factor in predictive maintenance that has an influence on the maintenance scheduling, spare parts prediction and also the operational performance of the assets. This paper aims to model and analyze the RUL of railcars at LKAB, a Swedish Mining Company. To achieve this goal, first the failure behavior of the critical subsystems was analyzed and discussed. Then, with considering the effective operational factors (covariates), proportional hazard model (PHM) is applied to calculate the reliability functions. In this concept, RUL of the railcars can be obtained and discussed at the various initial survival times.