“As an asset-intensive industry with 30-50% of operational expenses tied to maintenance, mining has a significant opportunity to flip that ratio – through an updated maintenance approach that enables more strategic asset management,” says MS Prakash, Emerson’s vice president for the African region.
Costs are highest when operators follow outdated reactive maintenance practices – doing routine checks and fixing things when they break. This method increases costs, downtime and risks to personnel.
Using modern technologies, predictive and prescriptive maintenance enable operators to monitor assets and accurately predict – and fix – potential problems before they occur. This approach not only supports asset reliability but also reduces risk to personnel safety associated with machine failure. More reliable equipment also means less energy consumption and reduced emissions.
The African mining market is witnessing a significant shift towards predictive maintenance with industries increasingly adopting advanced technologies to improve efficiency and reduce downtime. A prime example is the collaborative effort by Emerson to assist a large mining company in South Africa in digitalising its condition monitoring systems. Prakash says this initiative focused on critical equipment at two of the company's mining sites, addressing long-standing issues related to inefficient manual data collection and frequent maintenance.
“At one mine, the lack of monitoring and the reliance on intermittent manual data collection led to suboptimal productivity and safety concerns. Similarly, another mine faced challenges with inclined conveyers, experiencing considerable downtime and extended maintenance schedules.”
To tackle these issues, Emerson implemented the AMS 6500 – an online asset health monitoring system featuring adaptive tracking based on varying load and speed conditions. This technology provided real-time, high-quality data that was integrated into the company’s existing AMS Machinery Manager software – ensuring a seamless digital transformation.
Successful deployment of these advanced monitoring systems at both mines has significantly enhanced the mining company's operational efficiency. The shift from reactive to predictive maintenance has enabled pre-emptive actions, minimised downtime and optimised production processes. This transformation exemplifies the growing trend and confidence in predictive maintenance technologies across the African mining industry.
The path to prescriptive maintenance
The transition to prescriptive maintenance starts with more intelligent assets that are embedded with sensors and automation to boost visibility into processes. These intelligent assets produce valuable data that, over time, reveals trends engineers can use to predict potential issues and identify causes before failure occurs.
New mines can build this intelligence into their designs with integrated automation enabling a quicker transition to prescriptive maintenance. For existing mining facilities, the process requires more finesse with a phased approach based on ROI that prioritises installing sensors and automation in large, critical assets. Sensors and analytical automation form a foundation for the transition to prescriptive maintenance.
This automation ecosystem must prioritise interoperability to be effective. Proprietary tech “black boxes” keep equipment data siloed while open standard system protocols enable all asset operation and monitoring to be accessible to the entire organisation. That accessibility in turn allows remote monitoring and operations centres to provide all stakeholders access to necessary data. This collaboration enables engineering, process, reliability and metallurgical experts to make decisions that are best for the whole process rather than just one piece of equipment.
“Automation also improves asset reliability via machine learning platforms and advanced analytics that can merge process and reliability data to build algorithms tailored specifically to a given process – enabling maintenance decisions based on real conditions. And that data can flow upward, integrating into other higher-level systems to deliver greater operational efficiencies across entire enterprise systems,” Prakash says.
