Abstract
In the mining industry, the drive towards digital transformation is inevitable. The opportunity most spoken of nowadays as part of this drive to improve productivity and efficiency of the mining industry is that of predictive maintenance. When properly executed, predictive maintenance maximizes profits and minimizes risk of losses due to system failure, in addition to reducing the overall maintenance cost of capital-intensive mining equipment and machinery.
In large open pit or underground mines where number and size of assets in operation are usually high, forecasting equipment availability is a fundamental step in any effective management process. Hence adopting and executing the right maintenance strategy has become critical to the success of mining companies today. As such, there are two different maintenance approaches to be considered, which the global mining industry has traditionally relied upon: corrective maintenance (or run-to-failure) and preventive maintenance.
The Industrial Internet of Things (IIoT), enabled via wireless connectivity, is the main pillar of adopting a predictive maintenance strategy. It transforms physical actions from machines and real-time information on the surrounding environment into digital signals used by machine learning algorithms. Information from all these varying systems and sensors provides the basis for finalizing predictive maintenance approaches. IIoT and wireless connectivity must be integrated effectively with data science and modeling capabilities to reach the ultimate objective of digitalization.
Another important topic to thoroughly understand at the strategic planning stage is the interdependence of predictive maintenance and the dependability of IIoT and wireless connectivity. IIoT communications for predictive maintenance must satisfy strict requirements of accuracy and delay.