Predictive maintenance is a powerful tool for managing equipment and resources across various industries. It enables the prediction of potential failures and issues, allowing proactive measures to be taken to prevent them. This, in turn, leads to a reduction in unplanned downtime, lowers repair costs, and improves overall production efficiency.
 
A key component of predictive maintenance is the collection of data from diverse sources, such as programmable logic controllers (PLCs), inverters, sensors, and other equipment. Real-time analysis of this data using diagnostic algorithms helps detect deviations from normal functioning and sends notifications to operators or engineers about potential problems. According to a study by PwC, the implementation of predictive maintenance can result in an 8-12% reduction in equipment maintenance costs and a 20-25% decrease in spare parts expenses.
Long-term data archives and analytical tools enable in-depth analysis, helping to identify anomalies that may lead to failures in the future. By predicting failures, proactive measures can be taken to prevent unplanned downtime and extend the service life of equipment.
 
Calculating operating time and planning maintenance schedules is another vital aspect of predictive maintenance. This optimization of repair and part replacement schedules reduces the risk of failures and enhances production efficiency.
Furthermore, predictive maintenance contributes to minimizing energy losses and identifying hidden inefficiencies, facilitating the planning and implementation of effective energy-saving measures. By analyzing energy consumption data, it becomes possible to optimize energy usage and develop energy consumption benchmarks per unit of output.
 
The FACEPLATE platform offers an intuitive display of information in a user-friendly format, enabling operators and managers to visually assess trends and receive real-time dynamic reports. This assists in analyzing energy efficiency and integrating predictive maintenance systems with existing information systems. According to the International Energy Agency's Energy Efficiency Market Report 2020, predictive maintenance can reduce energy consumption by 10-20% by optimizing equipment operation and preventing energy losses.
 
In summary, predictive maintenance in the mining industry allows for improved control over product quality, reduced repair costs, increased energy efficiency, and minimized unplanned downtime. This contributes to more stable operations, risk reduction, and enhanced competitiveness in the market for enterprises implementing these practices.