PREDICTIVE MAINTENANCE IN THE MINING INDUSTRY

In the mining industry, the drive towards digital transformation is inexorable. 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 adequately executed, predictive maintenance maximizes profits. It minimizes the risk of losses due to system failure, in addition to reducing the overall maintenance cost of capital-intensive mining equipment and machinery.

Mining companies use massive equipment and huge vehicles in high-cost operations that can lose profitability if these machines break down. Hence maintaining these capital-intensive assets in optimal condition is critical to the safe and profitable process of the mine.

There are two different maintenance approaches that the global mining industry has traditionally relied upon: corrective maintenance (or run-to-failure) and preventive maintenance. Under the former, systems are operated until they fail, at which point repair or replacement actions are triggered. On the other hand, the latter encompasses all activities and processes implemented to maintain a system (and each component) in a specified state via inspection, detection, and avoidance of failures. Regular maintenance schedules are planned and executed with guidance from original equipment manufacturers (OEMs). Some OEMs even require adherence to such maintenance schedules to maintain warranty eligibility.

Preventing failures via regular maintenance actions and parts replacements in advance is cheaper than repairing unexpected failures. But the cost and effectiveness of such an approach to maintenance can be optimized by adopting a predictive maintenance approach. Under this approach, failures are predicted before they happen by analyzing the equipment data, typically collected via wirelessly connected sensors.

Predictive maintenance minimizes total system downtime via predictive analytics based on machine learning algorithms. In addition, it optimizes maintenance costs by avoiding unnecessary maintenance actions typically executed under a fixed schedule preventive maintenance plan. For example, valve adjustment of a mining haul truck under a preventative maintenance plan would generally be a 12-hour activity undertaken at regular intervals. Analysis of data collected via sensors on the vehicle, on intake and exhaust valve opening and closing events can help identify the specific valves that require adjustments and reduce the activity’s overall duration to under 3 hours. For a large mine, this translates into millions of dollars in savings.

The availability of such mining assets directly affects the planning of production capacity. In other words, asset availability has a direct relationship with production capacity. In large open-pit or underground mines where the 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 correct maintenance strategy has become critical to the success of mining companies today.

The Industrial Internet of Things (IIoT), enabled via wireless connectivity, is the central 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. Data on parameters, such as temperature and vibration, is continuously streamed from sensors, along with data from programmable logical controllers (PLCs), the mining execution system (MES) terminals, and computerized maintenance management systems (CMMSs). 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, which is supporting decision-making to act on physical systems optimally. But the misconception that predictive maintenance is always the best maintenance policy must be avoided. Implementing an IIoT-enabled predictive maintenance strategy can be costly and only makes sense if it is more profitable than other maintenance approaches. As with any other decision, the value of information collected via IIoT must be greater than the total cost of gathering that information. Hence the cost-effectiveness of the integrated end-to-end predictive maintenance solution, including the IIoT infrastructure and the wireless communications system(s), must be evaluated before making the strategic investment decision.

Another essential 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. Effective and safe behavior of the monitored system(s) based on predictive analytics can only be guaranteed if the exchanged information meets these requirements. Hence, the communications network must be designed and built to meet the needs of robustness to packet loss and delay. In addition, the dependability of IIoT and wireless communications also means that these networks must be safe to operate in hazardous mining environments and be resilient to physical damage.

It is important to note that an IIoT network is a highly complex system and can be considered a distributed cyber-physical system. Add the complexity introduced by the harshness of the environment these systems exist in, along with the legal and regulatory constraints of the mining industry, and evaluating the IIoT infrastructure’s dependability for predictive maintenance can become significantly challenging. Capturing such complexity in a meaningful manner demands that information on the IIoT infrastructure’s safety, reliability, resilience, and cyber security be integrated into predictive maintenance modeling. Deploying IIoT and wireless technology not fit for the mining industry ecosystems can exacerbate the challenges associated with predictive maintenance and overall digital transformation.

In the end, if there is one message that readers need to take from this article, it is that digital transformation of the mining industry demands the use of artificial intelligence and machine learning (AI/ML) to optimize operations, which requires big data, which demands dependable IIoT based on robust wireless connectivity. For a predictive maintenance approach to be practical, it must be executed with an end-to-end system engineering approach that encompasses all these technology components, i.e., a scalable and agile predictive analytics engine and a dependable IIoT infrastructure with a robust wireless communications system.

References:

Compare, M., Baraldi, P., Zio, E. “Challenges to IoT-Enabled Predictive Maintenance for Industry 4.0”. IEEE Internet of Things Journal (2020). Web.

Viana, H. R. G., Queiroz, A. N. A. “Availability Forecast of Mining Equipment.” Journal of Quality in Maintenance Engineering (2016). Web.

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