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Source publication
Purpose
It is well known in the mining industry that the increase in failures and breakdowns is due mainly to a poor maintenance policy for the equipment, in addition to the difficult access that specialized personnel have to combat the breakdown, which translates into more machine downtime. For this reason, this study aims to propose a remote assi...
Contexts in source publication
Context 1
... information collected by the satellite monitoring system allows users to have greater control of their operations, prolonging the life of machine components, reducing the likelihood of catastrophic failures and eliminating most unplanned downtime. Figure 1 shows the data transmission in Komatsu's Komtrax system. The satellite monitoring system for Komatsu high tonnage equipment (mining and production) allows remote access to equipment operation information and to know the condition of major components. ...
Context 2
... the set of highly complex faults requiring remote expert support is presented. In Figure 10, the behavior of truck fleet availability is shown in addition to the KPIs Mean Time Between Failures (MTBF), Mean Time to Repair (MTTR) and truck utilization in the last rolling year. Then, Figure 11 shows the Pareto charts according to the information entered daily into the reconciliation platform, with which it is determined which are the critical systems that affect the truck fleet. ...
Context 3
... Figure 10, the behavior of truck fleet availability is shown in addition to the KPIs Mean Time Between Failures (MTBF), Mean Time to Repair (MTTR) and truck utilization in the last rolling year. Then, Figure 11 shows the Pareto charts according to the information entered daily into the reconciliation platform, with which it is determined which are the critical systems that affect the truck fleet. Finally, Figure 12 shows the degree of unforeseen events of the trucks compared to the programmed percentage. ...
Context 4
... Figure 11 shows the Pareto charts according to the information entered daily into the reconciliation platform, with which it is determined which are the critical systems that affect the truck fleet. Finally, Figure 12 shows the degree of unforeseen events of the trucks compared to the programmed percentage. It can be seen that there is a high degree of unscheduled stops, which may indicate that there is a considerable amount of interruptions or unexpected events that affect the normal operation. ...
Context 5
... data collection is obtained from the detentions that occurred due to failures in Komatsu trucks, which are entered daily in the mine maintenance reconciliation system platform. Figures 13 and 14 show the reconciliation system that generates data that can be downloaded by date range and in different formats (PDF, XLS, CSV). The report allows identifying the date, equipment, stoppage time, type of maintenance, systems, subsystems, components and responsible company; this information is shown in Figure 15. ...
Context 6
... 13 and 14 show the reconciliation system that generates data that can be downloaded by date range and in different formats (PDF, XLS, CSV). The report allows identifying the date, equipment, stoppage time, type of maintenance, systems, subsystems, components and responsible company; this information is shown in Figure 15. Finally, Figure 16 presents the number of unscheduled stoppages the CAEX Komatsu fleet has had from 2016 to November 2021. ...
Context 7
... report allows identifying the date, equipment, stoppage time, type of maintenance, systems, subsystems, components and responsible company; this information is shown in Figure 15. Finally, Figure 16 presents the number of unscheduled stoppages the CAEX Komatsu fleet has had from 2016 to November 2021. It is worth mentioning that the selection of the data corresponds to the fleet of 930E-4SE extraction trucks, and these post-mortem results are the ones that make up the data set. ...
Context 8
... report that can be extracted from the reconciliation system has a preset format of 18 columns, which can only be modified by authorized IT&C personnel (see Figure 18). The columns describe the information entered by the maintenance schedulers regarding an unplanned outage or a scheduled outage. ...
Context 9
... it is not required at this stage to go into the details of the failure or the assignment of responsibility for the failure. Figure 17 shows the table of data that make up the reconciliation system. It shows the failures that occurred according to the operational day, duration, type of stoppage, component and subsystem affected, including their respective description and status. Figure 18 shows the final report to be used for learning, which allows a better understanding of the data. ...
Context 10
... shows the failures that occurred according to the operational day, duration, type of stoppage, component and subsystem affected, including their respective description and status. Figure 18 shows the final report to be used for learning, which allows a better understanding of the data. It details the operational day that the stoppage occurs, how long it lasts, the identifier of the associated equipment, and the component and subsystem affected. ...
Citations
Mining machinery constitutes essential assets for a mining corporation. Due to economies of scale, technological innovations and stringent quality and safety requirements, the size, complexity, functionality and diversity of industrial machinery have expanded markedly over the last two decades. This growth has increased sensitivity to machine availability and reliability. Mining operations install comprehensive maintenance units tasked with inspection, repair, replacement and inventory management for the machines in use. Leveraging the proliferation of sensor technologies integrated within the machines, maintenance units obtain rich data streams synchronously disclosing machine health and performance metrics, which enables a predictive maintenance programme. This programme performs prognostic detections of anomalies and permits timely intervention to avert catastrophic breakdowns. However, such sensor-driven predictive maintenance scheme for machinery in the mining sector is limited. The present paper utilises the Gaussian process, a powerful predictive modelling technique, to show its potential in addressing this challenge. The efficacy of this approach is validated through three case studies. Each case study is equipped with sensor data and represents a typical predictive maintenance task for mining assets. The developed Gaussian process models successfully capture meaningful temporal patterns in sensor data and generate credible predictions across all three tasks: temporal prediction of sensor data degradation trends, remaining useful lifespan prediction and simultaneous monitoring and prediction of multiple machine conditions. Furthermore, the models offer uncertainty estimates to the prediction outcomes, potentially facilitating maintenance decision-making process.
