The hostile environment and complex operation of mining industries are major drivers of the main problems faced by the sector: unplanned equipment downtime and high operational maintenance costs.

Because of this, the recent technological advances have begun to deliver more precise solutions to the challenge of keeping up with the industry’s demand for its products while seeking to reduce unnecessary expenses in the operation.

In this context, the predictive maintenance process will be presented and deepened in order to understand how it can be the solution to increase the efficiency and profitability of mining industries by facilitating data management in these companies.


How maintenance is usually performed in mining industries


Traditionally, most industries usually operate using two main types of maintenance: corrective (reactive) and preventive (calendar-based). The former follows the interference plan only when the problem in the structure or machinery has already occurred, while the latter takes into account a scheduled maintenance plan at certain time intervals, being carried out even with the equipment in optimal condition.

The problem with both techniques used is that they lead to very high operating costs, since they allow many unplanned shutdowns in the corrective method, and high expenses with unnecessary maintenance in the preventive method.

With the technological advance of Industry 4.0, keeping these high maintenance costs is no longer a prudent decision to assertively establish oneself in the market. As a result, another type of process began to emerge and gain space in the world’s largest mining companies: predictive maintenance.


Predictive maintenance



The rise of buzzwords such as Smart Mining or Mining 4.0 shows the increasing technological influence on the operation of these industries, and this influence has also affected maintenance processes.

Unlike the previously given examples, predictive maintenance begins to use technologies such as Artificial Intelligence and Machine Learning to create predictive failure models according to processed asset critical data. This information is usually collected by sensors in the field and connected in real-time with the many data storage systems used by these industries.

In the case of mining industries specifically, valuable machine vibration and operating temperature data, for example, can be processed automatically and indicate possible future problems with the assets in question.


Why predictive maintenance is essential for mining industries


Taking as a basis some equipment very present in the operation of mining industries, such as Grinding Mills and Conveyor Belts, the simple stoppage of this machinery because of an unidentified error could cost millions and stop an entire operation indefinitely.

According to McKinsey, predictive maintenance usually decreases between 30 to 50% of equipment stoppages in industries and increases their lifetime by 20 to 40%. In other words, investment in the related technologies allows for an improvement in the mean time between failure of equipment and greater longevity of the industry.

In the context of the mining industry, this means increasing equipment efficiency, preventing unplanned downtime, increasing overall safety, and, most importantly, reducing total industry costs.




In general, technological advances have created many benefits for the mining sector and, most importantly, have allowed for huge cost savings in one of the most important processes in any industry: maintenance.

In this case, predictive maintenance allows understanding the best moment to act in a process, perform the replacement of a part and even the complete replacement of equipment, and all this with much greater advance and planning time, increasing the profits of the operation.

In addition, the use of these technologies allows for not only greater asset efficiency but also greater structural integrity for the mining industries. Click here to learn more!

About the Author: Andre Andrade
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